APPLICATION OF DEEP CONVOLUTIONAL NEURAL NETWORK TO PREVENT ATM FRAUD BY FACIAL DISGUISE IDENTIFICATION
The paper proposes and demonstrates a Deep Convolutional Neural Network (DCNN) architecture to identify users with disguised face attempting a fraudulent ATM transaction. The recent introduction of Disguised Face Identification (DFI) framework proves the applicability of deep neural networks for this very problem. All the ATMs nowadays incorporate a hidden camera in them and capture the footage of their users. However, it is impossible for the police to track down the impersonators with disguised faces from the ATM footage. The proposed deep convolutional neural network is trained to identify, in real time, whether the user in the captured image is trying to cloak his identity or not. The output of the DCNN is then reported to the ATM to take appropriate steps and prevent the swindler from completing the transaction. The network is trained using a dataset of images captured in similar situations as of an ATM. The comparatively low background clutter in the images enables the network to demonstrate high accuracy in feature extraction and classification for all the different disguises.
 This paper is an application paper on detecting when a face is disguised, however it is poorly written and do not contribute much in terms of novelty of the approach. The application domain is interesting, however it is simply a classification problem The paper is written clearly (with mistakes in an equation), however, it does not contribute much in terms of novelty or new ideas. To make the paper better, more empirical results are needed. In addition, it would be useful to investigate how this particular problem is different than a binary classification problem using CNNs. Notes: Equation 2 has a typo, ***
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
APPLICATION OF DEEP CONVOLUTIONAL NEURAL NETWORK TO PREVENT ATM FRAUD BY FACIAL DISGUISE IDENTIFICATION
The paper proposes and demonstrates a Deep Convolutional Neural Network (DCNN) architecture to identify users with disguised face attempting a fraudulent ATM transaction. The recent introduction of Disguised Face Identification (DFI) framework proves the applicability of deep neural networks for this very problem. All the ATMs nowadays incorporate a hidden camera in them and capture the footage of their users. However, it is impossible for the police to track down the impersonators with disguised faces from the ATM footage. The proposed deep convolutional neural network is trained to identify, in real time, whether the user in the captured image is trying to cloak his identity or not. The output of the DCNN is then reported to the ATM to take appropriate steps and prevent the swindler from completing the transaction. The network is trained using a dataset of images captured in similar situations as of an ATM. The comparatively low background clutter in the images enables the network to demonstrate high accuracy in feature extraction and classification for all the different disguises.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 This paper is an application paper on detecting when a face is disguised, however it is poorly written and do not contribute much in terms of novelty of the approach. The application domain is interesting, however it is simply a classification problem The paper is written clearly (with mistakes in an equation), however, it does not contribute much in terms of novelty or new ideas. To make the paper better, more empirical results are needed. In addition, it would be useful to investigate how this particular problem is different than a binary classification problem using CNNs. Notes: Equation 2 has a typo, ***
assistant
 This paper is an application paper on detecting when a face is disguised, however it is poorly written and do not contribute much in terms of novelty of the approach. The application domain is interesting, however it is simply a classification problem The paper is written clearly (with mistakes in an equation), however, it does not contribute much in terms of novelty or new ideas. To make the paper better, more empirical results are needed. In addition, it would be useful to investigate how this particular problem is different than a binary classification problem using CNNs. Notes: Equation 2 has a typo, ***