Answers.Body: <p>AFAIK, normally detection algorithms work in a sub-window of the image and not the whole of it. For example, for a specific size and orientation you slide a sub-window on the image and extract sub-images. Then you apply your algorithm on every sub-image for detection and report the size-and-orientations with positive results.</p>
  
  <p>You can have a single neural network for face detection in this case or you might want to have different detectors for different orientation or any other feature, that is your decision.</p>
  
  <p>There is also the technique of <a href="https://en.wikipedia.org/wiki/Ensembles_of_classifiers" rel="nofollow">Combining Classifiers</a> by which you can improve the decision of single classifiers by combining them.</p>
  
  <p><a href="https://en.wikipedia.org/wiki/Ensemble_learning" rel="nofollow">Ensemble Learning</a> is another way in which your classifiers are not trained independently but rather together. In fact, the well-known <a href="https://en.wikipedia.org/wiki/Viola%E2%80%93Jones_object_detection_framework" rel="nofollow">object detector of Viola and Jones</a> uses such a technique.</p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2016-08-04T19:36:06.863Z
Answers.LastActivityDate: 2016-08-04T19:56:38.653Z
Answers.LastEditDate: 2016-08-04T19:56:38.653Z
Answers.UserDisplayName: Didami
Body: <p>Given pictures with multiple features such as faces, can a single AI algorithm detect all of them, or for better reliability is it preferred to use separate instances?</p>
  <p>In other words, I'm talking about an attempt of finding all possible human faces in the same picture by a single neural network.</p>
  
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2016-08-04T13:41:46.873Z
Comments.Text: Yes, finding several faces (unknown number of them) on one image by one AI network.
Comments.UserDisplayName: kenorb
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2016-08-04T21:00:12.923Z
Comments.Text: @bpachev Yes, I've added appropriate tag.
Comments.UserDisplayName: kenorb
Tags: <deep-neural-networks><algorithm><image-recognition>
Title: Do you need single or multiple networks to detect multiple faces?
UserDisplayName: kenorb
ThreadText: # Do you need single or multiple networks to detect multiple faces?
  
  (Asked by: username_0 on 2016-08-04)
  
  Given pictures with multiple features such as faces, can a single AI algorithm detect all of them, or for better reliability is it preferred to use separate instances?
  
  In other words, I'm talking about an attempt of finding all possible human faces in the same picture by a single neural network.
  
  **Tags:** deep-neural-networks, algorithm, image-recognition
  
  > **Comments:**
  > **username_0**: Yes, finding several faces (unknown number of them) on one image by one AI network.  
  > **username_0**: @bpachev Yes, I've added appropriate tag.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2016-08-04. Score: 3 | Accepted answer
  
  AFAIK, normally detection algorithms work in a sub-window of the image and not the whole of it. For example, for a specific size and orientation you slide a sub-window on the image and extract sub-images. Then you apply your algorithm on every sub-image for detection and report the size-and-orientations with positive results.
  
  You can have a single neural network for face detection in this case or you might want to have different detectors for different orientation or any other feature, that is your decision.
  
  There is also the technique of [Combining Classifiers](https://en.wikipedia.org/wiki/Ensembles_of_classifiers) by which you can improve the decision of single classifiers by combining them.
  
  [Ensemble Learning](https://en.wikipedia.org/wiki/Ensemble_learning) is another way in which your classifiers are not trained independently but rather together. In fact, the well-known [object detector of Viola and Jones](https://en.wikipedia.org/wiki/Viola%E2%80%93Jones_object_detection_framework) uses such a technique.
  
  
  ---
  
  