From my observations and little experience it appears that most of the ML project are about classifying stuff. Is there cancer signs on the photo? Does the picture show car, whale or banana? Etc.

I need to implement a model for face identification. Not detection/recognition, but identification: having two different photos of the same person, my model should determine if in the pictures is depicted the same person.

I want to achieve that using Tensorflow with convolutional nets. I've read this paper: http://ydwen.github.io/papers/WenECCV16.pdf and center loss looks promising. What do you think about that? Are there any new ideas/papers/implementations regarding that problem that are worth attention?

I asked this question also on MachineLearning reddit (https://www.reddit.com/r/MachineLearning/comments/8cysrx/d_what_are_the_stateoftheart_models_for/) and got an useful link with FaceNet implementation, trying here also :)

  • $\begingroup$ If you mean detection, are you familiar with YOLO? $\endgroup$ Apr 18 '18 at 13:42
  • $\begingroup$ @Media As I stated above, my net should identify person using two pictures, not only detect whether picture contain face. $\endgroup$
    – 3voC
    Apr 18 '18 at 14:14
  • $\begingroup$ Not exactly what you want but the idea may help you here. $\endgroup$ Apr 18 '18 at 14:23

EDIT : Deep Face Recognition: A Survey New on arxiv 4/18/2018 looks like best survey of methods over face related tasks :).

Beyond Facenet, there are a few approaches which may be good to look at how many faces do you intend to have your system know about - aka netowrks that have a network to directly output which face it is (~ <10K), vs a feature map for clustering (~ 10k to 100K), or comparing any 2 faces (~ >100K) - below are examples of approaches to each...

This paper just came out: Exploring Disentangled Feature Representation Beyond Face Identification- 2018 - Reported Accuracy 99.816 . Use an encoder-decoder like scheme to compute features of a face. Then given all the faces you computed this on, do clustering to find which ones are the same face (TSNE - distance in feature space). This paper is cool since it also gives features of each face like 'smiling', and these are used to augment the search. Along similar line is this

Before that (other than Facenet) - DeepFace - Accuracy 97.35. Its the facebook lib. If its state-of-the-art enough for them, its state of the art enough for me. Approach is given two images, put them into siamese network, for first detect, then 3d model the face, then project to 2d featuremap, which then combine to label saying whether they are same person or not.

Robust Face Recognition via Multimodal Deep Face Representation - 2016, Reported accuracy 98.43. This is interesting because they trained on a relatively small dataset (CASIA WebFace). However this has the last layer being the number of identities in the dataset - so this could be a llimiting factor if you want to recognize millions of identities like facebook does. Otherwise, this looks easiest to implement/mess with.

Patch-based Face Recognition using a Hierarchical Multi-label Matcher - not sure what is going on here, but looks interesting.

I would think another limiting factor for you is how many examples do you have per face - eg do you have 4K identities and 4mil images like the facebok dataset, or 10k identities and .5mil images (CASIA WebFace), or LFW with like ~5K identities and ~15k images.


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