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[D] Which is the better way to perform face recognition?

I want to use the MS Celeb dataset (100,000 classes) to label images of various celebrities. I was thinking of 2 ways to do this.

  1. Train a new face recognition model from scratch on this dataset with 100,000 classes as output.
  2. Use a pretrained model like face_recognition library to get the encodings of a face and compare it (L2 distance) with the encodings of all the 100,000 classes. The class whose encodings are closest to this face, will be the face’s class.

I don’t know if this ‘comparing’ method will work well with 100,000 classes (the encodings are of 128 dimensions). What do you guys think will be more feasible?

submitted by /u/Eoncarry
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.