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University of Florida College of Dentistry News: Accolades, Scholarships and Charity
The University of Florida College of Dentistry (UFCD) plays an important role for people in Gainesville in particular, but also for the profession of American dentistry in general. Research engine Start Class ranks UFCD among the top 10 dental schools in the country. That's why it's so selective: The college only enrolls about 90 new students each year. UFCD students and alumni aren't just among the brightest. They're also some of the most giving. From charitable works to scholarship news, here are some of the most important news and stories concerning the students and faculty at UFCD.
New Scholarships Launched
Reporter Adam Turner at the Alligator recently wrote about joy that comes from pain. After a two-year battle with brain cancer, 33-year-old UF alumnus Dr. Huy Huynh sadly passed. His family, however, have committed themselves to Huynh's memory and his "Triple Gator" legacy by starting a scholarship in his name. Huynh received his Doctor of Dental Medicine degree from UFCD, his bachelor's and master's from UF, and represented the student government as a cabinet director. Friends and family praised Huynh's ability to bring people together to celebrate life. Retired UFCD professor Marc Gale has also launched a new scholarship for students who stand up for LGBTQ rights. Gale has begun with an endowment fund of $42,000 and six $7,000 scholarships. Gale was originally going to launch the scholarship in his parents' name, but after the tragic massacre at Pulse nightclub in June 2016, the professor changed his mind. The Gale Pride Scholarship is to help fund UFCD students' tuition as long as they are somehow involved in the promotion and defense of LGBTQ rights, although they don't necessarily have to identify as LGBTQ themselves. Gale is not alone in his commitment to diversity and advocacy for rights. UFCD was recognised for the second consecutive year by INSIGHT Into Diversity magazine for its promotion of equality and diversity. The magazine is the longest-running publication focusing on issues of diversity in higher education. UFCD won the 2015 and 2016 Health Professions Higher Education Excellence in Diversity (HEED) Awards. Let's see them take the hat trick this year. It shouldn't be a problem if they remember the words of Dean A. Isabel Garcia, who says diversity is about a "culture of inclusion and acceptance throughout the dental school experience."
It's not just diversity awards being won by UFCD faculty and alumni. Dr. Ronald L. Dixon in St. Augustine has been recognized as a 2017 Top Doctor, dedicated to healthcare practitioners who have shown clinical excellence throughout their careers and high regard for patient treatment. Dixon has more than 40 years of experience in the field. That's a lot of teeth. He started his long-standing love affair with dentistry at UFCD, graduating in 1976 with a Doctor of Dental Medicine Degree. Dr. Roger B. Fillingim is also celebrating after his recent recognition with the Distinguished Service Award from the American Pain Society for his dedicated service. Fillingim is a professor in the Department of Community Dentistry & Behavioral Science and is also director of the UF Pain Research and Intervention Center of Excellence. Barry Setzer, D.D.S has taken home honors with the Public Service Award from the Florida Dental Association (FDA) for his service to the state. Bestowed with the honor at the 2017 FDA Annual Meeting in Orlando in June, Setzer was recognized for ongoing work in the field of dentistry. He also played an integral role in the 2016 Florida Mission of Mercy by organizing fundraising events. His work helped raise more than $590,000 in donations and in-kind revenue. From giving money to earning it, two UFCD students from the Class of 2018, Casey Cameron and Kelsey Ebach, have received scholarships from the Hinman Dental Society. The students were announced at the 105th Thomas P. Hinman Dental Meeting in Atlanta. The Hinman scholarship has been awarded to students from the southeast for the past 30 years, with the Hinman Dental Society contributing more than $8.5 million in scholarships in the past 17 years. Faculty member Bonita A. Wynkoop, D.D.S is also in the dentistry news after receiving a scholarship from the Council on Dental Education and Licensure. She is attending the Academy for Academic Leadership Institute for Teaching and Learning. Having joined UFCD in 2011 as a courtesy clinical assistant professor in the UF Department of Operative Dentistry, Wynkoop became a full-time staff member three years later. She is now team leader of the predoctoral clinics in the Division of General Dentistry in the Department of Restorative Dental Sciences.
Professional Excellence
Everyone loves to be recognized for a job well done, and dentistry professors are no different. Congratulations to five UFCD faculty members who have been promoted for the quality and impact of their work at the university, the profession and society.
Tarek El-Kerdani has been promoted to clinical professor in the UF Department of Restorative Dental Sciences Division of Prosthodontics.
Monica Fernandez is now clinical associate professor in the UF Department of Restorative Dental Sciences Division of Prosthodontics.
Nadim Islam has been promoted to clinical professor in the UF Department of Oral & Maxillofacial Diagnostic Sciences Division of Oral Pathology.
Rodrigo Neiva has been promoted to clinical professor in the UF Department of Periodontology.
Rodrigo Souza is now clinical associate professor in the UF Department of Community Dentistry and Behavioral Science.
Good Deeds Getting Noticed
The NCEF Pediatric Dental Center continues its good work and shows UFCD's commitment to social upliftment. Born from a collaborative effort between UFCD, the Naples Children & Education Foundation, the Healthcare Network of Southwest Florida, and Florida Southwestern, the center opened its doors in 2008 to treat kids of Southwest Florida. The center now treats around 3,000 children a year. It is also a prestigious place for applicants seeking pediatric dental residencies. Every year, more 200 applicants vie for the five spots, according to Dr. Lauren Governale, clinical director. When they pick the residents, they pick well. Four residents have scored in the top 3 percent nationally on the qualifying exam with the American Academy of Pediatric Dentistry. Twenty-four UFCD students were also involved earlier this year in the The Mission of Mercy clinic, organized by the Florida Dental Association Foundation. More than 1,000 dental professionals and students visited Pensacola from all over Florida to give free dental care to those in need. UFCD Students, residents, assistants and faculty also traveled to Jasper a few months ago to provide free dental care to athletes at the Special Olympics. The help formed part of the Special Smiles portion of the Special Olympics Healthy Athletes program. Finally, Super Sealant Saturday is a student-led event to provide free preventative dental care for local children. In April, UFCD students helped close to 200 kids with their dental issues with free dental exams, dental sealants and fluoride varnish. They even put on a superhero and educational carnival. The event, now in its third year, was co-founded by Stephen Obeng, D.M.D., and Patrick Fitzgerald, D.M.D. Images by: Lesly B. Juarez, Peter Ksprzyk, Samuel Zeller, Mi PHAM


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Q: Would stringify an already rounded float number and then convert it back to number create further precision error? Accrodading to Is floating point math broken?, I know 0.1+0.2 results in 0.30000000000000004 instead of 0.3 because of floating point errors. However, what if I copy "0.30000000000000004" from alert(0.1+0.2) and write Number("0.30000000000000004"), would it still equal to 0.1+0.2?
Or in general:
let a=float_number_1 + float_number2;
let b=Number((float_number_1 + float_number2).toString());

Does a always exactly equal to b?

A: Java's default conversion of a number to a string is specified to produce enough digits to uniquely distinguish the number from adjacent representable values. In other words, it is deliberately designed to produce a string that can be converted back to a number to produce the original value.
So, if you convert from a float to a string to a float or from a double to a string to a double, you will get the original number. If you convert a double to a string to a float, you may get a different number.

redpajama_set_name: RedPajamaStackExchange


Nature has an interesting article about a potential source of Dark Energy in the universe (free access here for a short time) whereas photons over long distances can turn into "chameleon" particles in the presence of magnetic fields. In the process, these particles loose some mass and appear more dim then they would normally.
The real link here is a short discussion on the paper from Zapperz which is very readable and interesting!
Published on June 1, 2009 at 11:00AM.
Has CDMS Found Dark Matter?


Type: Right, A: 3-7/8", B: 1-15/16", C: 1-15/16", D: 2", E: 3/16"
Type: Left, A: 3-7/8", B: 1-15/16", C: 1-15/16", D: 2", E: 3/16"
Chrome plated bronze. Use two left or two right hand hinges for quick removal of hatches, motor covers, or together as a heavy-duty hinge.
Removable feature allows motor box to be pulled apart for engine access.
Type: Left or Right, A: 3-1/2", B: 1-3/4", C: 1-3/4", D: 2", E: 3/16"
Take Apart Hinge 1.5" x 2.75"
Take apart hinge w/8 holes.
A: 1-1/2", B: 3/4", C: 3/4", D: 2-3/4", E: 1/16"


NORWALK (CNS) – A man in his 20's was shot and killed in what authorities said was a gang-related shooting this afternoon in Norwalk.
The sheriff's Norwalk Station received a report just before 2 p.m. about a gunshot victim in the 13000 block of Liggett Street, near the intersection of Rosecrans and Shoemaker avenues. Deputies arrived and found the man down in the street, suffering from multiple gunshot wounds, according to Deputy Trina Schrader of the Los Angeles County Sheriff's Department.
The victim was taken to a hospital, where he was later pronounced dead, Schrader said. His name was not released.
Anyone with information on this shooting was asked to call the sheriff's Homicide Bureau at (323) 890-5500 or Crime Stoppers at (800) 222-8477.


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Espuri Carvili (en llatí Spurius Carvilius) va ser un magistrat i polític romà del . Formava part de la gens Carvília, una gens romana d'origen plebeu.

Va ser elegit tribú de la plebs de l'antiga Roma l'any 212 aC juntament amb Luci Carvili, que podria ser el seu germà. Els dos tribuns van llençar una acusació contra Marc Postumi per haver defraudat l'estat fingint pèrdues en els subministraments de materials i gra que enviava a les tropes romanes durant la Segona guerra púnica.

Referències

Tribuns de la plebs del segle III aC

redpajama_set_name: RedPajamaWikipedia


When I saw that Inspiration was the theme of WP Weekly Photo Challenge, I decided to sleep over it before searching for images.
What is inspiration? Beyond definitions of dictionaries, what does this word tell me individually?
Having narrowed down my definition of inspiration, as the strong force that touches me deeply, and compels me to do the unthinkable and the impossible, I asked myself another question: what and who inspires me?
An easy and obvious answer would be to tell the world who inspires me the most are the ones I love. My husband, daughter, parents and siblings, nephews and nieces, in-laws, friends, etc…..but then the gallery of photos started to become too big. Well, better leave it for another blog post.
There are also other people, who I may not love as those mentioned above, but who inspire me for who they are and for the good that they have done to the world. Martin Luther King, Gandhi, and so on and so forth. Well, that would deserve another post and I had never photographed them.
And how about those perhaps less famous people, who I don't know personally, but their books or blogs? Some bloggers may even be reading this post now, not even knowing how much they inspire me and teach me everyday. These are people who, no matter how simple or complex their actions, have had the courage to transform their lives and of others, showing the same determination and focus to follow their dreams. They are a source of constant inspiration. I would need to ping back many of you, and mention many book titles here, but would have no photos. I found an alternative solution though, with this post.
Have I done justice to inspiration yet? No, because I hadn't yet covered what inspires me.
My list started to become complex as I thought of work and leisure. No, let me not go there yet. You have no time for such a humongous blog post.
Simplicity was the answer I was looking for. If I look straight in the mirror, and search for what moves me beyond measure in whatever action I take, in any area of my life, inspired by any of these people mentioned above, I would find a constant factor, expressed in a few words.
Explore. Connect. Learn. Grow. Help. Share. Dare to do it.
What inspires me will have to come from any, or most, of the above words, translated from the experiences and thoughts of many people.
I'm happy with the answers to my quest for inspiration. But how about the photos?
I went for a Monday walk in the park with Jo, from Restless Jo, to find my inspirational photos. I brought my Olympus camera with me. I was enjoying the walk and had no subject in mind. Suddenly, a coffee Latte and a chocolate cake saw me, and invited me to seat at the nearby cafe.
There I was watching nature and people, connecting like a magnet.
As I changed the lens to a 60mm (equivalent 120mm), I spotted kids and an elegant woman approaching fast. I shot without looking at the settings, hoping it was in auto, because I could miss her behind the tree. Well, it wasn't in auto…I like her movement though.
It was exciting to see her coming to the cafe, and I hoped she would come to the terrace where I sat.
I was thrilled when she entered and I hoped she would choose a table next to me. She found one a few meters behind me. I would need the macro lens indeed, and to sit in another chair with my back to the park so that I could focus on her. I took a few minutes observing her. She was waiting for the waitress whilst contemplating the park's landscape. I made the first photo, not using the view finder, but placing the camera on my lap at the height of the armchair, so that I could discreetly shoot without her awareness.
I found her face so beautiful, strong, and expressive. Every wrinkle, every line, every curve in total harmony with the accessories she used. She looked like someone who knows what she wants and she goes for it. As the waitress approached, I saw how gracious and soft her face became. This is a woman who knows love. Who can help and share.
As she waited for her order, she removed her sun glasses and started browsing her smart phone.
Her face became more severe, perhaps for reading something that worried her or that she didn't like. Her expression now openly speaking of a life full of lessons, growth, actions, mistakes and dreams that came true.
She then read something that softened her face. A discreet smile evoking happiness.
As the waiter arrived, she put the phone on the table and grabbed her sun glasses. She then took a very satisfying sip of coffee, and focused on reading her book. Totally connected and immersed in her activity. Happy with herself. And so was I.
This post may not make sense to you, maybe not even to me. I built it in my mind, while writing in free flow, and deciding for which photos to use. I shared with you what I consider as the most inspiring force that compels me into photography: People and the lives I imagine for them, while reading their facial expressions.
I am aware of shooting them anonymously, and I am grateful and honored to portray them in this blog, with my utmost respect.
For those who asked me a few posts ago, why I post black and white portraits, I have especially added a few color photos.
I am posting for the Photo Rehab. The community to share photos, learn from each other and connect. Want to know more about it?
thank you…amazing the way you connected with this woman…not hidden behind the camera, but with your eyes through the camera's eye.
Thank you Brenda, it's great to see that you connected with my thoughts too.
You are, of course, Lucile, an inspiration to all of us, through your beautiful photos and well considered words as always!
Heartfelt thanks, Andy. Every word touches me deeply and make me grateful for your friendship.
Thank you, that also means a lot to me.
You have a very unusual outlook on life, Lucile (and I mean that in a good way 🙂 ) I might observe in this fashion but it would not occur to me to capture it on camera. People are endlessly fascinating, aren't they? None more so than you. We never know where you're taking us next, but are just grateful for the experience.
Jo, that's the best compliment I could wish for. An unusual outlook is what I strive to have in my life, believing that as the way to find my own voice, from the ashes of mainstream beliefs and standardized thinking. It's a daily reminder I have to make to myself.
And I'm overwhelmed and humbled by so much praise. Heartfelt thanks!
I'm always inspired when walking your way!
Thank you so much! You know how to use the right words!
Your encouraging comments mean a lot! Thanks.
I love this! It's beautiful. I feel like we are together sharing a coffee when I read one of these types of posts from you. I love the story you have told here through the photographs. You're right of course – she is a strong and beautiful woman who knows what she wants and usually figures out how to get it. Mastery again Ninja-Lu xox.
I wish we had had that chat and coffee! That's why I made that pic I sent via whataapp.
All these warm words make me miss our chats and my beloved friend.
Me too. Very much. You must come for a visit. Xoxoxox.
What a fascinating insight, Lucile. A collection of photos that in their very particular nature reveal the universal. Inspiring!
Heartfelt thanks for such generosity, Jill. Your comment humbles me.
I want the like button back so I can keep hitting it for Andy's statement! Can I tell you how much I loved this post?! Yes, I did. How fascinating to read both your words and this woman's expressions and think, yes, Lucile is exactly spot on. Color, B&W–it is all beautiful. And inspiring. And I chose cats…..
But the like button is always there on the reader! This one WP doesn't let me remove.
But I love to be loved! Thank you zillions. Why don't they add a love button instead?
Seriously, I'm humbled by your lovely and generous words. Heartfelt thanks!
And cats are inspiring to you, so?
I loved reading your post and the portraits and your interpretations of them are delightful. You have inspired me to try your "camera on the lap' technique. Not sure how you were able to capture her face from this angle. I'll probably end up with "belly shots"!
There you can bend the camera too if you need. If still not high enough you then use your handbag as an extra support. Let me know how you did it! Good luck!
That is such a lovely read Lucile, very insightful and honest. You are an inspiration believe me!
Your comment touches my heart. Thanks, Debbie.
Your post makes perfect sense Lucile. Love how you take in and embrace every little detail of your surroundings, in this case, the dear lady at the outdoor café.
Hi Carl, thanks for the lovely visit and comment.
Always good to feel your generosity here.
Loved this post! And your ability to capture the essence of a person with your portraits is marvellous….
You perfectly captured her quest for inspiration and in turn inspired all of us! Fabulous!
This is a wonderful post seeing strength where so many see weakness especially in the western world. We send our aged to nursing homes to shirk our responsibility. In life we soon may have to take that seat. Will our actions stare us in the face!!
Thank you Rê, and welcome to the blog. Huge hug.
The first photo of the park does not bear your copyright. Is it yours?


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Bharti and Wal-Mart to split retail JV; Wal-Mart to buy local partner in wholesale retail unit
Wal-Mart Stores Inc is buying out Bharti Enterprises in their equal equity wholesale retail joint venture Bharti Walmart Pvt Ltd, marking a split in their retail venture, as per a joint statement issued on Wednesday.
The breakup of the venture means that now Wal-Mart is free to tie with another local retailer while Bharti can go ahead and bring another foreign retailer as a partner for its existing retail chain.
Upon receipt of required clearances, Wal-Mart would acquire Bharti's stake in Bharti Walmart, the joint venture between Bharti and Wal-Mart, giving Wal-Mart 100 per cent ownership of the Best Price Modern Wholesale cash and carry business.
Bharti Walmart presently operates around 20 wholesale stores in India under the Best Price Modern Wholesale brand. FDI up to 100 per cent is allowed in wholesale retail business.
Rajan Bharti Mittal, vice chairman and MD, Bharti Enterprises, said: "Bharti is committed to building a retail venture and will continue to invest in Bharti Retail across all formats. We believe that with our current footprint of 212 stores, we have a platform to grow the business."
Bharti independently operates neighbourhood retail chains under the Easyday brand spread across the north India region besides states like Maharashtra in the west and Karnataka in the south. The Bharti-Wal-Mart JV currently also manages the backend of Easyday chain as a logistics partner. It is not clear if that arrangement will continue after the two partners decided to end the alliance.
Scott Price, president and CEO Wal-Mart Asia said, "Given the circumstances, our decision to operate independently will be beneficial to both parties. Through Wal-Mart's investment in India, including our cash and carry business, supply chain infrastructure, direct farm programme and supplier development, we want to serve India. Wal-Mart is committed to businesses that serve our members and provide good returns for our shareholders, and we will continue to advocate for investment conditions that allow FDI in multi-brand retail in India."
Simultaneously, Bharti will acquire the compulsory convertible debentures (CCDs) held by Wal-Mart in Cedar Support Services, a firm owned and controlled by
Bharti-run Easyday. The firm has been under government scanner for possible violation of FDI norms. While Wal-Mart did not own stake in the firm as FDI was not allowed in multi-brand retail till recently, it held convertible securities to buy a stake.
The government recently opened up the retail sector to foreign investors allowing FDI up to 51 per cent in multi-brand retail and 100 per cent for single-brand retail.
For Bharti this comes amidst a string of such marquee global deals starting with Vodafone for telecom and AXA for insurance and asset management, which came unstuck.
Vodafone, which picked a minority stake in Bharti Airtel years ago, later decided to go ahead and buy controlling stake in Hutchison Essar.
(Edited by Joby Puthuparampil Johnson)
Wal-Mart Stores Inc.

redpajama_set_name: RedPajamaCommonCrawl


Can we make a sailboat that is articulated and inflatable at the same time? Yes.
Can we make a prototype that is simple and modular to test different size, shapes and control on the same machine? Yes.
Interesting model that allowed us to verify the possibility of having a boat that is both inflatable and articulated. Now it is not easy, the articulations needs to be very well executed else it creates too much friction for the servos motors to handle. Protei_005.1 did not really function because the servos were too weak and there was too much friction on the cables. The main point was to get familiar with these inflatable new prototyping materials.
Materials : Plastic film from "space bag"


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Bohnenkamp column: Bohannon is all about the big shots
IOWA CITY — The 3-pointers were almost from the same spot on different courts.
One came on Thursday night to seal Iowa's win at Indiana.
One came on Sunday night to cap a 15-point comeback against Northwestern.
"It's been fun," Jordan Bohannon said of the week that was.
It's not a surprise. For two-plus seasons Bohannon has been the biggest of big-shot makers for the Hawkeyes.
He is who he is, and the Hawkeyes know that.
"He gets ready for clutch shots like that," Iowa coach Fran McCaffery said after Sunday's 80-79 win over the Wildcats at Carver-Hawkeye Arena. "He's prepared his whole life for this opportunity."
"He hit his shot," said forward Tyler Cook, shrugging his shoulders like it was no big deal because Bohannon's teammates have seen it before.
The shot in the 77-72 win at Indiana and the one against Northwestern not only came from basically the same distance, they looked almost the same. They are those no-no-yes shots that some players take, but for Bohannon they are just part of his game.
"I have always felt comfortable with those shots," said Bohannon, who scored all 15 of his points in the second half against the Wildcats, 13 of those coming after the Hawkeyes were down by 15 points with 4:30 to play. "It's something I practice — one dribble to the right, to the left, leaning shots. We drew up a good set and got the ball in my hands."
Bohannon runs Iowa's offense, which can be a good thing and a bad thing at the same time. He controls the action, but it means the opposing defense knows where he is going to be.
"Give credit to Northwestern, they were up in his face," McCaffery said. "That's kind of how teams are playing him. So as the game goes on, you got to loosen him up, move him off the ball, get him in some different kinds of actions. Sometimes it's sets, sometimes it's motion. We run a couple different motions, late-game action. Now he's in different locations.
"If he's bringing it down the floor, they know where he is, they can get up into his face. We set a ball screen for him, they can trap it, limit his shot opportunities."
Because of that, he can sometimes become a non-factor. But then he can have the bursts like he did against Northwestern and at Indiana, when he scored the Hawkeyes' final 11 points.
What impressed McCaffery on Sunday night is something he has continually noted about Bohannon — he doesn't put pressure on himself to get involved when he's struggled.
"To his credit," McCaffery said, "he didn't put up any bad (shots)."
Bohannon was one of four starters taken out of the game a little more than three minutes into the second half, scolded by McCaffery for their effort on both ends of the court.
"It would have been really easy for (McCaffery) to sit me for the rest of the game, for how I was playing," Bohannon said. "He was really confident in me, really confident in everyone that was out there that we were going to pull this off.
Bohannon mocked his ability to come up with big shots — "Ice in my veins, right?" he said, smiling at the cliché — but it is clear he thrives in such moments.
Think back to his freshman year, when his 3-pointer with nine seconds left gave Iowa a 59-57 win at Wisconsin.
The big shots have fallen ever since, and the rest of the Big Ten knows it.
"That's what he does," Northwestern coach Chris Collins said. "He is a late-game player."
"Any time we have a shot like that, an opportunity like that, I want the ball in my hands," Bohannon said.
It's why McCaffery never has a no-no-yes thought about a Bohannon shot. The coach knows his point guard will never panic.
"I would say this — I haven't had too many like him, that have that kind of stroke, consistently make shots like that," McCaffery said. "I've had a few. I will say this — the ones that I've had wanted to shoot it in that situation. They have such supreme confidence in their ability to make it, they want to shoot it.
"That's who he is."


Whitecaps formed by waves breaking and air entrainment are dynamic features on the ocean surface which evolve quickly and thus have markedly different properties in different lifetime stages. Active whitecaps (aka stage A or young whitecaps) accompany the turbulent mixing in the moment of wave breaking. In contrast, residual (aka stage B or mature) whitecaps are almost motionless foam patches left behind the breaking wave. The relative contributions of active and residual whitecaps to various air-sea interaction processes differ. Therefore, to properly parameterize modifications of momentum, heat, and mass transfers across the air-sea interface in presence of breaking waves, it is desirable to separately quantify the fraction of the ocean surface covered by active and residual whitecaps.
We pursue the separation of active whitecap fraction WA from total (active plus residual) whitecap fraction W with two approaches. Physical basis of our theoretical approach is the Phillips concept of breaking wave statistics which connects active WA with the energy dissipation rate of breaking waves. Physical basis of our experimental approach is the distinct signature of active and residual whitecaps at infrared (IR) wavelengths. To this end, a multi-instrumental field campaign was conducted in April-May, 2012, on the Floating Instrument Platform (FLIP). The instruments deployed included sensors recording the whitecaps and breaking waves on the surface over wide range of wavelengths: visible (video cameras), infrared (IR camera), microwave (radiometers at two frequencies, 10 GHz and 37 GHz), and acoustic (vertical line array of hydrophones at frequencies of 600, 1200, and 2400 Hz). The main goal of the field campaign was to verify the previous serendipitous observations of active and residual whitecaps as bright and dark patches in IR images and correlate them with time series of other observations.
We will present results from processing and analyzing IR images from the FLIP measurements. We identified 28 events of breaking waves from both visible and IR images in which active and residual whitecaps are clearly distinguished in IR images, but not in the visible. We use the IR images to quantify the lifetime stages and characterize properties of active and residual whitecaps. With such knowledge, we can use IR observations as a tool to identify separation criteria in radiometric and acoustic data which are suitable for remote sensing and long-term monitoring of breaking waves.


text:
\section{INTRODUCTION}
Keypoint detection on unconstrained  faces is one of the most studied topics in the past decade, as accurate localization of fiducials is a vital pre-processing task for variety of applications. In the last five years, keypoint localization using Convolution Neural Networks (CNN) has received great attention from computer vision researchers. This is mainly due to the availability of large scale annotated unconstrained face datasets such as AFLW\cite{tugraz:icg:lrs:koestinger11b}.
Works such as\cite{DBLP:journals/corr/ZeilerF13} have hypothesized that as the network gets deeper more abstract information such as identity, pose, attributes are retained while immediate local features are lost. However, various methods \cite{Sun:2013:DCN:2514950.2516090},\cite{CFAN}, and \cite{Zhu_2015_CVPR} directly use CNNs as regressors or use deep features from CNNs to design regressors for predicting keypoints.

On the other hand, an earlier method of Explicit Shape Regression (ESR) proposed by Cao et al.\cite{DBLP:journals/ijcv/CaoWWS14} achieved superior results by introducing the important concept of non-parametric shape regression for facial keypoint localization. Following \cite{DBLP:journals/ijcv/CaoWWS14}, unconstrained face alignment received great deal of attention and many of its variants \cite{7299048},\cite{DBLP:conf/cvpr/RenCW014},\cite{kazemi2014one},\cite{Sun:2013:DCN:2514950.2516090},\cite{DBLP:journals/corr/KumarRPC16} were published later, using a variety of features producing incremental improvements over \cite{DBLP:journals/ijcv/CaoWWS14}. However, they are all limited by the fixed number of points on the face. In real life applications, there are more challenging datasets such as IJBA\cite{F_2015_CVPR} and AFW\cite{AFW_dataset_CVPR2012}, which do not always have 68 or 49 fixed points mainly due to occlusion or pose variations. As alternatives, researchers moved towards more sophisticated techniques, incorporating 3D shape models\cite{DBLP:journals/corr/ZhuLLSL15},\cite{lfa3d},\cite{pifa}, domain learning\cite{Zhu_2016_CVPR}, recurrent autoencoder-decoder\cite{recdec} and many others. However, one question still remains unanswered: Can cascaded shape regression be applied for an arbitrary face with no prior knowledge ?

\begin{figure}[t]
\centering
\includegraphics[height=1.7cm,width=8.7cm]{3.jpg}\\
\includegraphics[height=1.7cm,width=8.7cm]{4.jpg}
\caption{\small Sample results generated by the proposed method. White dots represent the location of keypoints after each iteration. The first row shows an image from the AFLW dataset. The points move at subpixel level after fourth iteration. The second row is a sample image from the AFW dataset, which shows how the last stage of error correction can effectively mitigate the inconsistency of the bounding box across datasets. The numbers in red are the predicted 3D pose P:Pitch Y:Yaw R:Roll }
\label{fig:sample}
\end{figure}
The motivation for this work is to adapt cascaded regression for predicting landmarks of arbitrary faces, while taking advantage of CNNs. We transform the cascaded regression formulation into an iterative scheme for arbitrary faces. In each iteration the regressor predicts the increment for the next stage jointly for all the points while maintaining the shape constraint. As by-products of KEPLER, we get the visibility confidence of each keypoint and 3D pose (pitch, yaw and roll) for the face image.
The main contributions of this paper are:
\begin{itemize}
\item We design a novel GoogLenet-based\cite{c2} architecture with a channel inception module which pools features from intermediate layers and concatenates them similar to inception module. We call the proposed architecture \textit{Channeled Inception} in the rest of the paper. This network is used in all the stages of KEPLER.
\item Inspired by \cite{c1}, we present an iterative method for estimating the face landmarks using the fixed point consolidation scheme inspired by \cite{c1}. We observe that estimating landmarks on a face is more challenging than estimating keypoints on a human body.
The overview of the pipeline is shown in Figure \ref{fig:method}.
\item After each iteration, the error from ground-truth decreases, making the gradient smaller and hence different training policies are employed in every stage for the efficient training of H-CNN.
\item We evaluate the performance of our keypoint estimation method on challenging datasets such as AFLW and AFW, which include faces in diverse poses and expressions. We also introduce a new protocol for evaluating the facial keypoint localization scheme on the AFLW dataset which is more challenging and usually left out while evaluating unconstrained face alignment methods.
\end{itemize}
The rest of the paper is organized as follows. Section II reviews closely related works. Section III presents the proposed method in detail. Section IV describes the experiments and comparisons, which are then followed by conclusions and suggestions for future works in section V.

\section{RELATED WORK}
Following \cite{DBLP:journals/ijcv/CaoWWS14}, we classify previous works on face alignment into two basic categories. \\ \\
\textit{\bf{Part-Based Deformable models:}} These methods perform alignment by maximizing the confidence of part locations in a given input image. One of the major works in this category was done by Zhu and Ramanan\cite{AFW_dataset_CVPR2012}, where they used a part-based model for face detection, pose estimation and landmark localization assuming the face shape to be a tree structure.\cite{Asthana:2013:RDR:2514950.2516059} by Asthana et al., learned a dictionary of probability response maps followed by linear regression in a Constrained Local Model (CLM) framework. Hsu et al.\cite{7410796} extended the mixture of tree model \cite{AFW_dataset_CVPR2012} to achieve better accuracy and efficiency. However, their method again assumes face shape to be a tree structure, enforcing  strong  constraints  specific to shape  variations. \\
\textit{\bf{Regression-based approaches:}} Since face alignment is naturally a regression problem, a multitude of regression-based approaches has been proposed in recent years. Methods reported in \cite{conf/eccv/LiangXWS08},\cite{DBLP:journals/ijcv/CaoWWS14},\cite{Zhu_2015_CVPR} are based on learning a regression model that directly maps image appearances to target outputs. However, these methods along with methods from \cite{antonakos2016adaptive},\cite{trigeorgis2016mnemonic},\cite{antonakos2015active},\cite{akshay_wild},\cite{6909635} and \cite{6619290} were mostly evaluated either in a lab setting or on face images where all the facial keypoints are visible. Wu et al.\cite{7410774} proposed an  occlusion-robust cascaded regressor to handle occlusion.  Xiong et al.\cite{global2015xiong} pointed out that standard cascaded regression approaches such as Supervised Descent Method (SDM)\cite{XiongD13} tend to average conflicting gradient directions resulting in reduced performance. Hence,\cite{global2015xiong} suggested domain dependent descent maps. Inspired by this, Cascade Compositional Learning (CCL)\cite{Zhu_2016_CVPR} and Ensemble of Model Regression Trees (EMRT)\cite{DBLP:journals/corr/ZhuLLT15} developed head pose based and domain selective regressors respectively. \cite{Zhu_2016_CVPR} partitioned the optimization domain into multiple directions based on head pose and learned to combine the results of multiple domain regressors through composition estimator function. Similarly \cite{DBLP:journals/corr/ZhuLLT15} trained an ensemble of random forests to directly predict the locations of keypoints whereafter face alignment is achieved by aggregating the consensus of different models.\par
Recently, methods using 3D models for face alignment have been proposed. PIFA\cite{pifa} by Jourabloo et al.  suggested a 3D approach that employed cascaded regression to predict the coefficients of 3D to 2D projection matrix and the base shape coefficients. Another recent work from Jourabloo et al.\cite{lfa3d} formulated the face alignment problem as a dense 3D model fitting problem, where the camera projection matrix and 3D shape parameters were estimated by a cascade of CNN-based regressors. However, \cite{Zhu_2016_CVPR} suggests that optimizing  the  base  shape  coefficients and projection is indirect and sub-optimal since smaller parameter errors are not necessarily equivalent to smaller alignment errors. 3DDFA\cite{DBLP:journals/corr/ZhuLLSL15} by Zhu et al. fitted a dense 3D face model to the image via CNN, where the depth data is modeled in a Z-Buffer.

Our work principally falls in the category of regression-based approaches and addresses the issue of adapting the cascade shape regression to unconstrained settings. KEPLER performs joint training on three fundamental tasks, namely, 3D pose, visibility of each keypoint and the location of keypoints, using only 2D color image. It also demonstrates that efficient joint training on the three tasks achieves superior performance. One of the closely related work is \cite{DBLP:conf/eccv/ZhangLLT14} where the authors used multi-tasking for many attributes, but did not leverage the intermediate features

\begin{figure}[htp!]
\centering
\includegraphics[height=5.25cm,width=5.25cm]{outline.JPG}
\caption{\small Overview of the architecture of KEPLER. The function \textit{f()} predicts visibility, pose and the corrections for the next stage. The representation function \textit{h()} forms the input representation for the next iteration.}
\label{fig:method}
\end{figure}

\section{KEPLER}

\begin{figure*}[htp!]
\centering
\includegraphics[width=14cm,height=6.5cm]{network.JPG}
\caption{The KEPLER network architecture. The dotted line shows the channeled inception network. The intermediate features are convolved and the responses are concatenated in a similar fashion as the inception module. Tasks such as pose are abstract and contained in deeper layers, however, the localization property is in the shallower layers. }
\label{fig:network_arch}
\end{figure*}

KEPLER is an iterative method which at its core consists of three modules. Figure \ref{fig:method} illustrates the basic building blocks of KEPLER. The first module is a rendering module \textit{h} which models the structure in an N-dimensional input space, with N being the maximum number of keypoints on a face. The current location of the keypoints are represented by the vector $\Vect{y_{t}}=\{y_{t}^{1}\ldots y_{t}^{N}\}$. The output of the rendering module is concatenated to the raw RGB input image $\Mat{I}$, along the third dimension which is then fed to the function \textit{f}.\\
The second module is the function \textit{f} which calculates the correction to be made at the next stage. The function \textit{f} is modeled by a convolution neural network whose architecture is described in  section \ref{netarch}. \\
The third module is the correction stage which adds the increments, predicted by \textit{f}, to the current locations. The output goes again into the rendering module \textit{h} which prepares the rendered data for the next iteration. The rendering function is not learned in this work, but represented by a 2D Gaussian with fixed variance and centered at current keypoint locations in each of the N channels. Finally, the Gaussian rendered images are stacked together with image $\Mat{I}$. Therefore the overall method can be summarized by the following set of equations.
\begin{eqnarray}
\Vect{\delta_{t}} = f_{t}(\Mat{X_{t}},\Theta_t) \\
\Vect{y_{t+1}} = \Vect{y_{t}} + \Vect{\delta_{t}} \\
\Mat{X_{t+1}} = h(\Vect{y_{t+1}})
\end{eqnarray}
where \textit{f} is a function with learned parameters $\Theta_t$, predicting the increments $\Vect{\delta_{t}}$ . The prediction function \textit{f} is indexed by \textit{t} as it is trained separately for every iteration. In the first iteration, the function \textit{h} renders Gaussians at $y_0$, which is the mean shape. In this work we set $t=5$ iterations. We perform the last iteration only to take into effect the improper bounding box across different datasets (see Figure \ref{fig:sample}). The loss functions for each task is mentioned below.\\
\textbf{Keypoint localization}\\
Keypoint localization is the task of predicting the keypoints in a face. In this paper, we consider predicting the locations of \textit{N} = 21 keypoints on the face. With each point is associated the visibility of that point. The loss function for this task is given by
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\setlength{\abovedisplayskip}{5pt}
\begin{equation}
L_{1}(\Vect{y},\Vect{g}) = \sum_{i = 1}^{N} v^{i}(y_{t}^{i} - g^{i})^{2},
\end{equation}
where $y_{t}^{i}$ and $g^{i}$ are the predicted and the ground truth locations of the $i^{th}$ keypoint resprectively at time $t$. $v^{i}$ is the ground truth visibility associated with each keypoint. We discuss this loss function and its variant in section \ref{stage3}. \\
\textbf{Pose Prediction}\\
Pose prediction refers to the task of estimating the 3D pose of the face. We use the Euclidean loss function for pose prediction.
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\setlength{\abovedisplayskip}{6pt}
\begin{equation}
L_{2}(\Vect{p}_p,\Vect{g}_p) = (p_{yaw}-g_{yaw})^{2} + (p_{pitch}-g_{pitch})^{2} + (p_{roll}-g_{roll})^{2}
\end{equation}
where $p$ stands for predicted and $g$ for the ground-truth.\\
\textbf{Visibility}\\
This task is associated with estimating the visibility of each keypoint.The number of keypoints visible on the face varies with pose. Hence, we use the Euclidean loss to estimate the visibility confidence of each point.
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\setlength{\abovedisplayskip}{6pt}
\begin{equation}
L_{3}(\Vect{v}_p,\Vect{v}_g) = \sum_{i = 1}^{N} (v_{p,i} - v_{g,i})^{2},
\end{equation}
Therefore the net loss in the network is the weighted linear combination of the above loss functions.
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\setlength{\abovedisplayskip}{10pt}
\begin{equation}
L(p,g) = \lambda L_{1}(\Vect{y},\Vect{g}) + \mu L_{2}(\Vect{p}_p,\Vect{g}_p) + \nu L_{3}(\Vect{v}_p,\Vect{v}_g)
\end{equation}
where $\lambda$, $\mu$ and $\nu$ are the weight parameters suitably chosen depending on the iteration.

\subsection{Network Architecture}
\label{netarch}
\begin{figure}[h]
\centering
\includegraphics[width=2cm,height=2cm]{1_s1.jpg}\includegraphics[width=2cm,height=2cm]{2_s1.jpg}\includegraphics[width=2cm,height=2cm]{3_s1.jpg}\includegraphics[width=2cm,height=2cm]{4_s1.jpg
\caption{\small Qualitative results of KEPLER after second stage. The green dots represent the predicted points after second stage. Red dots represent the ground truth. It can be seen that the visible  points have taken the shape of input face image.}
\label{fig:stage2}
\end{figure}
For the modeling function \textit{f} we design a unique ConvNet architecture based on GoogLenet\cite{c2} by pruning the inception network after inception\_4c. As PReLU has shown better performance in many vision tasks such as object recognition\cite{DBLP:journals/corr/HeZR015}, in this pruned network we first replace the ReLU non-linearity with PReLU.  We pool the intermediate features from the pruned GoogLenet. Then convolutions are performed from the output of each branch, and the output maps are concatenated similar to the inception module. We call this module the \textit{Channeled Inception} module. Since the output maps after $conv_{1}$ are larger in size, we first perform $4X4$ convolution and then again a $4X4$ convolution, both with the stride of 3 to finally match the dimension of the output to $7X7$. Similarly after $conv_{2}$ we first perform $4X4$ convolution and then $3X3$ convolution to match the output to $7X7$. The former uses a stride of 4 and the latter uses 2. The most n\"aive way of combining features is by concatenation. However, the concatenated output blob can be very high dimensional and hence we perform $1X1$ convolution for dimensionality reduction. This lets the network decide the weights to effectively combine the pooled features into lower dimension. It has been shown in \cite{DBLP:journals/corr/ZeilerF13} that adjacent layers are correlated and hence, we only pool features from alternate layers.\par
Next the network is trained on three tasks namely, pose, visibilities and the bounded error using ground truth. The joint training is helpful since it models the inherent relationship between visible number of points, pose and the amount of correction needed for a keypoint in particular pose. Choosing an architecture like GoogLenet is based on the fact that due to fewer number of parameters the training of GoogLenet is faster and adding to it batch normalization, even speeds up the training process. In order to further speed up the process we only use convolution layers till the last layer where we use a fully connected layer to get the final output.
The architecture of the whole network is shown in Figure \ref{fig:network_arch}.

\subsection{Iteration 1 and 2: Constrained Training}

In this section, we explain the first stage training for keypoint estimation. The first stage is the most crucial one for face alignment. Since the network is trained from scratch, precautions have to be taken on what the network should learn. Directly learning the locations of keypoints from a network is difficult because when the network gets deeper it loses the localization capability. This is due to the fact that the outputs of the final convolution layers have a larger receptive field on the input image. We devise a strategy in which the corrections for the first two stages are bounded.
Let us suppose the key-points are represented by their 2D coordinates $\Vect{y} :\{ \textit{y}^{i}\in \Re^{2}, i\in[1,\ldots,N]\}$ where \textit{N} is the number of keypoints and $y^i$ denotes the $i^{th}$ keypoint. The bounded corrections were calculated using (\ref{berr}) given below.
\setlength{\belowdisplayskip}{6pt}
\setlength{\abovedisplayskip}{6pt}
\begin{equation}
\label{berr}
\delta_{t}^{i}(g^i, y^{i}_{t}) = min(L,\|\Vect{u}\|).\hat{\Vect{u}}
\end{equation}
where \textit{L} denotes the bound of correction. $\Vect{u} = \Vect{g} - \Vect{y_{t}}$ and $\hat{\Vect{u}} = \frac{\Vect{u}}{\|\Vect{u}\|}$ represent the error vector and error unit vector respectively. In our experiments we set the bound $L$ to a maximum of 20 pixels. This simplifies the learning problem for the network in the first stage. According to this formulation, error correction for points for which the ground truth is far away, gets bounded by \textit{L}. The interesting property of this formulation is that in the first and second stage the network only learns the direction in which the points have to shift. This can be thought of as learning the direction of the unit error vector, to which the magnitude will be added later. In addition to just having keypoint location we also have access to facial 3D pose and the visibility of each point. One-shot prediction of the location of keypoints is difficult since the input space of the ConvNet is typically nonlinear. Also, learning small corrections should be easier, when the network is being trained for the first time. Hence, to impart prior knowledge to the network we jointly learn the pose and visibility of each point. The loss functions used for the three tasks are described in the previous section.\\
The function \textit{f} for second iteration is trained in a similar fashion with the weights initialized from the first iteration.

\subsection{Iteration 3: Variant of Euclidean loss}
\label{stage3}
We show the outputs of the network after the second stage of training in Figure \ref{fig:stage2}. Physical inspection of the outputs shows that for many of the faces, the network has already learned the magnitude and direction of the correction vector. However, there are misalignments in some images or in some keypoints in the images. But repeating the training methodology exactly as second iteration revealed that our architecture suffered from vanishing gradients. While back propagating the gradients, the loss is averaged over a batch and if there are few misalignments in a batch, there is very little gradient to be propagated. To maintain consistency we stick with the same architecture. Even though GoogLenet\cite{c2} claims to not have vanishing gradient problem, KEPLER faced it because of the dataset being small.

This motivated us to design a loss function that satisfies both of these conditions: on the one hand, the loss function should minimize the error between prediction and the ground truth; on the other hand, it should have sufficient gradients to be propagated to make the learning process reach global minima. Towards this end, we use the following loss function.
\setlength{\belowdisplayskip}{2pt}
\setlength{\abovedisplayskip}{2pt}
\begin{eqnarray}
\label{loss}
L_{1}(\Vect{y},\Vect{g}) = \frac{1}{n}\left(\sum_{i = 1}^{N} v_{i}(y_{i} - g_{i})^{2} + \gamma \sum_{i = 1}^{N} v_{i}\mid y_{i} - g_{i}\mid\right) \\
\frac{\delta L_{1}(\Vect{y},\Vect{g})}{\delta \Vect{y}} = \frac{1}{n}\left(2 \sum_{i = 1}^{N} v_{i}(y_{i} - g_{i}) + \gamma \sum_{i = 1}^{N} v_{i}\frac{\mid y_i - g_i \mid}{y_i - g_i}\right)
\end{eqnarray}
where $\gamma$ is a parameter which controls the strength of the gradient and \textit{n} is the number of samples in a batch. We would like to emphasize that the additional term is not a regularizer as it is added to the objective function and does not directly regularize the weights. However, this is able to provide substantial gradients for the training of ConvNet.  \\
The representation function \textit{h} in this stage does not render any Gaussian in the channel for which the predicted visibility is below the threshold $\tau$. In this work we set this threshold $\tau$ to 0.03 and $\gamma$ to 0.2 obtained by cross validation. We do not constrain the amount of error corrections for the third stage training.
\subsection{Iteration 4: Hard sample mining}
\label{stage4}
Recently, Kabkab et al.\cite{c4} suggested that by efficiently sampling the data one can make an optimal use of training set while training ConvNets leading to improved performance. \cite{c4} developed an online data sampling method based on a convex optimization formulation and showed how their formulation can make the classifier robust in class imbalanced problem. In our case, although after the third iteration, most of the images are aligned, they lack precision in local alignment. Inspired by \cite{c4}, we reuse the hard samples of the dataset to build a more robust keypoint localization system. \par
\begin{figure}[htp!]
\centering
\includegraphics[height=4cm,width = 8cm]{error_hist_s3_v2.jpg}
\caption{\small Error Histogram of training samples after stage 3}
\label{fig:error_hist}
\end{figure}
Using the keypoints predicted after the third iteration, we plot the histogram (Fig.\ref{fig:error_hist}) of normalized mean error (NME), after calculating it for all the training samples. We denote the NME on x-axis around which the maximum number of samples are centered, as \textit{C}. In an ideal case, the value of \textit{C} should be low, implying that the average alignment error is less. Therefore, the objective of this stage is to lower the value of \textit{C} by hard sample mining. We select a threshold $\Delta$ ($0.03$ in our experiments), towards the right of \textit{C}, after which at least $30-40\%$ of the samples lie, as the threshold for hard samples. Using $\Delta$, we partition the dataset into two groups of hard and easy samples. We first select equal number of samples from both groups to form a batch which is then presented to ConvNet for training. This effectively results in reusing the hard samples. Then, to counter the group imbalance we finetune the network with entire dataset again with a lower learning rate. We use the loss function as in (\ref{loss}) with $\gamma = 0.1$ for this stage
\subsection{Iteration 5: Local Error Correction}
\label{stage5}
There is a lot of inconsistency among the bounding boxes provided by different datasets. AFLW\cite{tugraz:icg:lrs:koestinger11b} provides larger bounding box annotations compared to AFW\cite{AFW_dataset_CVPR2012}. Regression-based alignment methods are dependent on the mean shape initialization, which is scaled to the bounding box size. Also it is impractical to come up with a heuristic which tries to determine compatible bounding boxes. Almost all the existing methods perform data augmentation by randomly perturbing the bounding boxes by some amount. However, it is not clear by how much the bounding boxes should be perturbed to obtain reasonably good bounding boxes during testing which is consistent with the dataset the network was trained on. We train our networks on a larger bounding box provided by AFLW. AFLW bounding boxes tend to be square and for almost all the images the nose tip appears at the center of the bounding box. This is a big limitation for the deployment of the system in real world scenarios. It is worthy to note that the previous four stages are trained on full images and hence produce global corrections.
\begin{figure}[htp!]
\centering
\includegraphics[height=2.5cm,width = 8cm]{stage52.JPG}
\caption{\small Red dots in the left image represent the ground truth while green dots represent the predicted points after the fourth iteration. Local patches centered around predicted points are extracted and fed to the network. The network shown in Fig \ref{fig:network_arch} (see section \ref{stage5} for details) is trained on the task of local fiducial correction and visibility of fiducials inside the patch. The image on the right shows the predictions after local correction. }
\label{fig:stage5net}
\end{figure}
Our last stage of local correction is optional, which depends upon the test set and the bounding box annotations that it comes with. We train an exactly similar network as in Fig \ref{fig:network_arch}. but only for the tasks of predicting the visibility and corrections in the local patches. Predicting the pose with a local patch of say $WXW$ pixels is difficult which can lead the network to learn improper weights. We choose all the $N$ patches irrespective of the visibility factor. Learning visibility and corrections is important because we do not want the network to propagate any gradient if the point is invisible. We observe during experimentation that training the ConvNet on two tasks together achieves significantly better performance than when the network is trained only for the task of error correction. We again partition the dataset into easy and hard sample groups according to the strategy explained in the previous section. We finally finetune the network with the whole dataset with lower learning rate.

\section{EXPERIMENTS AND COMPARISON}

\subsection{Datasets}

We select two challenging datasets with their most recent benchmarks.  \\
\textbf{\textit{In-the-wild datasets:}} To make the system robust for images in real life scenarios such as challenging shape variations and significant view changes, we select AFLW\cite{tugraz:icg:lrs:koestinger11b} for training and, AFLW and AFW\cite{AFW_dataset_CVPR2012} as the main test sets. \textbf{AFLW} contains $24,386$ in-the-wild faces (obtained from \textit{Flickr}) with head  pose ranging from $0\degree$ to $120\degree$ for yaw  and upto $90\degree$ for  pitch and roll with extremely challenging  shape  variations  and  deformations.  Along with this AFLW also demonstrates external-object occlusion. There are a total of 21\% invisible landmarks caused by occlusion, larger than 13\% on COFW\cite{10.1109/ICCV.2013.191} where only internal object-occlusion is exhibited.
In addition, one important point to note is that COFW also provides the annotations for the invisible landmarks while in the case of AFLW the invisble landmarks are absent. \textbf{AFW} is a popular benchmark for the evaluation of face alignment algorithms. AFW contains 468 in-the-wild faces (obtained from Flickr) with yaw degree up to $90\degree$ . The images are well diverse in terms of pose, expression and illumination. The number of visible points also varies depending on the image, but the location of occluded points are to be predicted as well.\\
AFLW provides at most 21 points for each face. It  excludes coordinates for invisible landmarks, which we consider to be the best, because there is no way of correctly knowing the exact location of those points. In many cases such invisible points are mostly hallucinated and annotated thereafter. \\   \\
\textbf{{Testing Protocols:}} \\ \\
\textbf{(I)AFLW-PIFA:} We follow the protocol used in PIFA\cite{pifa}. We randomly select $23,386$ images for training and the remaining $1,000$ for testing. We divide the testing images in three groups as done in \cite{pifa}: $[0\degree,30\degree]$, $[30\degree,60\degree]$ and $[60\degree,90\degree]$ where the number of images in each group are taken to be equal.\\
\textbf{(II)AFLW-Full:} We  also test on the full test set of AFLW of sample size $1,000$. \\
\textbf{(III)AFLW-All variants:} In the next experiment, to have more rigorous analysis, we perform the test on all variants of images from (I) above. To create all variants images, we first rotate the whole images from (I) at angles of $15\degree$,$30\degree$,$45\degree$ and $60\degree$. We do the same with the horizontally flipped version of these images. We then rotate the bounding box coordinates and the key-points also at the same angles and crop the faces. This is done for all the images following the AFLW-PIFA protocol. One important effect of this rotation is that some of the images have smaller face compared to others due to rotated bounding box. This experiment tests the robustness of the algorithm on faces of different effective sizes and orientations.\\
\textbf{(IV)AFW:} We only use AFW for testing purposes. We follow the protocol as stated in \cite{AFW_dataset_CVPR2012}. AFW provides 468 images in total, out of which 341 faces have height greater than 150 pixels. We only evaluate on those 341 images following the protocol of \cite{AFW_dataset_CVPR2012}.\\ \\
\textbf{Evaluation metric:}  Following  most  previous  works, we obtain the error for each test sample via averaging normalized errors for all annotated landmarks. We demonstrate our results with mean error over all samples, or via Cumulative Error Distribution (CED) curve. For pose, we evaluate on continuous pose predictions as well as their discretized versions rounded to nearest $15\degree$. We report the continuous mean absolute error for the AFLW testset and plot the Cumulative Error Distribution curve for AFW dataset. All the experiments including training and testing were performed using the Caffe\cite{jia2014caffe} framework and two Nvidia TITAN-X GPUs. Our method can process upto 3-4 frames per second, which can be higher in batch mode.

\begin{table}[h]
\centering
\begin{tabular}{lll}
\hline
 & {{\textbf{\textit{AFLW}}}} & {{\textbf{\textit{AFW}}}} \\
\hline
{{\textbf{Method}}} & {{\textbf{NME}}}  & {{\textbf{NME}}}\\ \hline
TSPM \cite{AFW_dataset_CVPR2012}   & -     & 11.09   \\
CDM \cite{Xiang_iccv_2013}         & 12.44    & 9.13          \\
RCPR \cite{10.1109/ICCV.2013.191}        & 7.85     &-          \\
ESR \cite{DBLP:journals/ijcv/CaoWWS14}         & 8.24    & -          \\
PIFA \cite{pifa}        & 6.8          & 8.61      \\
3DDFA \cite{DBLP:journals/corr/ZhuLLSL15}       & 5.32      &-         \\
LPFA-3D \cite{lfa3d}     & 4.72  & 7.43             \\
EMRT \cite{DBLP:journals/corr/ZhuLLT15}     & 4.01    & 3.55            \\
CCL \cite{Zhu_2016_CVPR}         & 5.85       & 2.45        \\
Rec Enc-Dec\cite{recdec}  & \textgreater6        & -        \\ \hline
\textbf{KEPLER}       & \textbf{2.98}   & 3.01    \\ \hline
\end{tabular}
\caption{\small {Comparison of KEPLER with other state of the art methods. NME stands for normalized mean error. For AFLW, numbers for other methods are taken from respective papers following the PIFA protocol. For AFW, numbers are taken from respective works published following the protocol of \cite{AFW_dataset_CVPR2012}.}}
\label{aflw_table}
\end{table}

\begin{figure}[htp!]
\centering
\includegraphics[height=5.5cm,width=8cm]{aflw_fids_everything.jpg}
\caption{\small Cumulative error distribution curves for landmark localization on
the AFLW dataset. The numbers in the legend are the average normalized mean error normalized by the face size.}
\label{aflw_res}
\end{figure}

\subsection{Results}
\label{com}
Table \ref{aflw_table} compares the performance of KEPLER compared to other existing methods. Table \ref{kepler_summary} summarises the performance of KEPLER under different protocols of AFLW testset. Table \ref{aflw_pose} shows the mean error in degrees, in estimating the 3D pose of a face image. Figures \ref{aflw_res} and \ref{afw_res} show the cumulative error distribution in predicting keypoints on the AFLW and AFW test sets. Figure \ref{afw_pose} shows the cumulative error distribution in pose estimation on AFW.

\textbf{Comparison with CCL\cite{Zhu_2016_CVPR}}: It is clear from the tables that KEPLER outperforms all state of the art methods on the AFLW dataset. It also outperforms all state of the art methods except CCL\cite{Zhu_2016_CVPR} on the AFW datatset. Visual inspection of our results suggests that KEPLER is a little farther from ground truth on invisible points. We note that CCL\cite{Zhu_2016_CVPR} manually annotates the AFLW dataset with 19 landmarks along with the invisible landmarks, leaving the earpoints. In our experiments we prefer to use the dataset as provided by AFLW\cite{tugraz:icg:lrs:koestinger11b}, although we believe that CCL-kind of reannotation may boost the performance(since during AFW evaluation the locations of occluded points also need to be predicted). In KEPLER there is no loss propagated for the invisible points. We believe that training KEPLER on the revised annotation by \cite{Zhu_2016_CVPR} would make the prediction of occluded points more precise.  \\
\begin{table}[h]
\centering
\resizebox{\columnwidth}{!}{%
\begin{tabular}{lllll}
\hline
{\textbf{Method}} & {\textbf{AFLW-PIFA}} & {\textbf{AFLW-FULL}} & {\textbf{AFLW-Allvariants}} & {\textbf{AFW}}\\ \hline
\textbf{KEPLER}   & 2.98 & 2.90 & 2.35 & 3.01     \\ \hline
\end{tabular}%
}
\caption{\small Summary of performance on different protocols of AFLW and AFW by KEPLER.}
\label{kepler_summary}
\end{table}

\begin{table}[h]
\centering
\resizebox{\columnwidth}{!}{%
\begin{tabular}{c|cccc|c}
\cline{1-6}
&  \multicolumn{4}{ c| }{\textbf{AFLW}} &\multicolumn{1}{c}{\textbf{AFW}} \\ \cline{2-6}
\textbf{Method} &  \textbf{Yaw} & \textbf{Pitch} & \textbf{Roll} & \textbf{MAE} & $\bf{Accuracy(\leq15\degree)}$ \\ \cline{1-6}
\multicolumn{1}{c|}{Random Forest\cite{Valle2016}} &
\multicolumn{1}{c|} - & - & - & 12.26\degree & 83.54\%     \\ \cline{1-6}
\multicolumn{1}{c|}{\textbf{KEPLER}} &
\multicolumn{1}{c|} {\bf{6.45\degree}} & \textbf{5.85\degree} & \textbf{8.75\degree} & \textbf{6.45\degree} & \textbf{96.67\%}  \\ \cline{1-6}
\end{tabular}%
}
\caption{\small Comparison of Mean error in 3D pose estimation by KEPLER on AFLW testset. For AFLW \cite{Valle2016} only compares mean average error in Yaw. For AFW we we compare the percentage of images for which error is less than 15\degree.}
\label{aflw_pose}
\end{table}
We also verify our claim that iteration 5 is optional and only required for transferring the algorithm to other datasets with different bounding box annotations. To support our claim we calculate the normalized mean error after iteration 4 for both datasets and compare with the error obtained after iteration 5. The error after iteration 4 for AFLW testset was 0.0369 (which is already lower than all existing works) and after fifth iteration it was 0.0299, bringing the performance up by $18\%$. On the other hand the improvement in AFW (whose bounding box annotation is different from AFLW) was close to $60\%$. The error after iteration 4 on AFW dataset was 0.0757 which decreases to  0.0301 after fifth iteration. \\
We demonstrate some qualitative results from AFLW and AFW test sets in Figure \ref{fig:allstage5}.
\begin{figure}[htp!]
\centering
\includegraphics[height=5.5cm,width=8cm]{afw_landmark_v2_all.jpg}
\caption{\small Cumulative error distribution curves for landmark localization on
the AFW dataset. The numbers in the legend are the fraction of testing
faces that have average error below (5\%) of the face size.}
\label{afw_res}
\end{figure}

\begin{figure}[htp!]
\centering
\includegraphics[height=5.5cm,width=8cm]{afw_pose.jpg}
\caption{\small Cumulative error distribution curves for pose estimation on AFW
dataset. The numbers in the legend are the percentage of faces that are
labeled within $\pm15\degree$ error tolerance}
\label{afw_pose}
\end{figure}

\section{Conclusions and Future Works}
In this work we show that by efficiently capturing the structure of face through additional channels, we can obtain precise keypoint localization on unconstrained faces. We propose a novel \textit{Channeled Inception} deep network which pools features from intermediate layers and combines them in the same manner to the Inception module. We show how cascade regressors can outperform other recently developed works. As a byproduct of KEPLER, 3D facial pose is also generated which can be used for other tasks such as pose dependent verification methods, 3D model generation and many others. In conclusion, KEPLER demonstrates that by improved initialization and multitask training, cascade regressors outperforms state of the art methods not only in predicting the keypoints but also for head pose estimation.
\begin{figure*}[htp!]
 \centering
\includegraphics[width=2cm,height=2cm]{AFLW1_s5}\includegraphics[width=2cm,height=2cm]{AFLW12_s5}\includegraphics[width=2cm,height=2cm]{AFLW8_s5}\includegraphics[width=2cm,height=2cm]{AFLW10_s5}\includegraphics[width=2cm,height=2cm]{AFLW9_s5}\includegraphics[width=2cm,height=2cm]{AFLW6_s5}\includegraphics[width=2cm,height=2cm]{AFLW4_s5}\includegraphics[width=2cm,height=2cm]{AFLW11_s5}\includegraphics[width=2cm,height=2cm]{AFLW13_s5}\\
\includegraphics[width=2cm,height=2cm]{AFW1_s5}\includegraphics[width=2cm,height=2cm]{AFW12_s5}\includegraphics[width=2cm,height=2cm]{AFW8_s5}\includegraphics[width=2cm,height=2cm]{AFW10_s5}\includegraphics[width=2cm,height=2cm]{AFW9_s5}\includegraphics[width=2cm,height=2cm]{AFW6_s5}\includegraphics[width=2cm,height=2cm]{AFW2_s5}\includegraphics[width=2cm,height=2cm]{AFW11_s5}\includegraphics[width=2.25cm,height=2.1cm]{AFW13_s5}\\
\includegraphics[width=2cm,height=2cm]{allvar1_s5}\includegraphics[width=2cm,height=2cm]{allvar3_s5}\includegraphics[width=2cm,height=2cm]{allvar4_s5}\includegraphics[width=2cm,height=2cm]{allvar5_s5}\includegraphics[width=2cm,height=2cm]{allvar6_s5}\includegraphics[width=2cm,height=2cm]{allvar7_s5}\includegraphics[width=2cm,height=2cm]{allvar8_s5}\includegraphics[width=2cm,height=2cm]{allvar9_s5}\includegraphics[width=2cm,height=2cm]{allvar10_s5}\\
\includegraphics[width=2cm,height=2cm]{allvar2_s5}\includegraphics[width=2cm,height=2cm]{allvar11_s5}\includegraphics[width=2cm,height=2cm]{allvar12_s5}\includegraphics[width=2cm,height=2cm]{allvar13_s5}\includegraphics[width=2cm,height=2cm]{allvar14_s5}\includegraphics[width=2cm,height=2cm]{allvar15_s5}\includegraphics[width=2cm,height=2cm]{allvar16_s5}\includegraphics[width=2cm,height=2cm]{allvar17_s5}\includegraphics[width=2cm,height=2cm]{allvar18_s5}
\caption{\small Qualitative results of KEPLER after last stage. The green dots represent the final predicted points after fifth iteration. First row are the test samples from AFLW. Second row shows the samples from AFW dataset. The last two rows are the results of KEPLER after last stage from AFLW testset for all variants protocol. The green dots represent the final predicted points after fifth iteration.}
\label{fig:allstage5}
\end{figure*}

\section{Acknowledgment}
This research is based upon work supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via IARPA R\&D Contract No. 2014-14071600012. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation
thereon.

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