Answers.Body: <p>This should not matter that much as long as you do enough preprocessing?</p>
  <p>But one could use e.g. the Facenet architecture to extract the embedding vector for each face of the same subject.</p>
  <p>Then you could compute the covariance matrix and perform PCA on it. This would give you the most significant features with which you can decide which face to take.</p>
  <p>Alternatively, you could do something in the direction of the eigenfaces: <a href="https://en.wikipedia.org/wiki/Eigenface" rel="nofollow noreferrer">https://en.wikipedia.org/wiki/Eigenface</a>.</p>
  
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Answers.CreationDate: 2022-01-29T20:16:29.970Z
Answers.Text: Could you elaborate on how to decide which face to take?
Answers.UserDisplayName: NikoNyrh
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Answers.CreationDate: 2021-01-04T14:33:02.267Z
Answers.LastActivityDate: 2021-01-04T14:33:02.267Z
Answers.UserDisplayName: claire_fontaine
Body: <p>Let us imagine a face database with several subjects, each subject having multiple face images. How do we determine which is the best face suitable for face recognition purposes?</p>
  
Tags: <deep-learning><face-recognition>
Title: In a face database containing multiple images per subject, how do we determine the face image which is most suited for face recognition?
UserDisplayName: user42346
ThreadText: # In a face database containing multiple images per subject, how do we determine the face image which is most suited for face recognition?
  
  (Asked by: username_0 on 2020-11-17)
  
  Let us imagine a face database with several subjects, each subject having multiple face images. How do we determine which is the best face suitable for face recognition purposes?
  
  **Tags:** deep-learning, face-recognition
  
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  ## 1 answers
  
  ### Answer by username_1 on 2021-01-04. Score: 0
  
  This should not matter that much as long as you do enough preprocessing?
  
  But one could use e.g. the Facenet architecture to extract the embedding vector for each face of the same subject.
  
  Then you could compute the covariance matrix and perform PCA on it. This would give you the most significant features with which you can decide which face to take.
  
  Alternatively, you could do something in the direction of the eigenfaces: <https://en.wikipedia.org/wiki/Eigenface>.
  
  > **Comments:**
  > **username_2**: Could you elaborate on how to decide which face to take?  
  
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