FaceNet: A Unified Embedding for Face Recognition and Clustering FaceNet: A Unified Embedding for Face Recognition and Clustering
Paper summary ## Keywords Triplet-loss , face embedding , harmonic embedding --- ## Summary ### Introduction **Goal of the paper** A unified system is given for face verification , recognition and clustering. Use of a 128 float pose and illumination invariant feature vector or embedding in the euclidean space. * Face Verification : Same faces of the person gives feature vectors that have a very close L2 distance between them. * Face recognition : Face recognition becomes a clustering task in the embedding space **Previous work** * Previous use of deep learning made use of an bottleneck layer to represent face as an embedding of 1000s dimension vector. * Some other techniques use PCA to reduce the dimensionality of the embedding for comparison. **Method** * This method makes use of inception style CNN to get an embedding of each face. * The thumbnails of the face image are the tight crop of the face area with only scaling and translation done on them. **Triplet Loss** Triplet loss makes use of two matching face thumbnails and a non-matching thumbnail. The loss function tries to reduce the distance between the matching pair while increasing the separation between the the non-matching pair of images. **Triplet Selection** * Selection of triplets is done such that samples are hard-positive or hard-negative . * Hardest negative can lead to local minima early in the training and a collapse model in a few cases * Use of semi-hard negatives help to improve the convergence speed while at the same time reach nearer to the global minimum. **Deep Convolutional Network** * Training is done using SGD (Stochastic gradient descent) with Backpropagation and AdaGrad * The training is done on two networks : - Zeiler&Fergus architecture with model depth of 22 and 140 million parameters - GoogLeNet style inception model with 6.6 to 7.5 million parameters. **Experiment** * Study of the following cases are done : - Quality of the jpeg image : The validation rate of model improves with the JPEG quality upto a certain threshold. - Embedding dimensionality : The dimension of the embedding increases from 64 to 128,256 and then gradually starts to decrease at 512 dimensions. - No. of images in the training data set **Results classification accuracy** : - LFW(Labelled faces in the wild) dataset : 98.87% 0.15 - Youtube Faces DB : 95.12% .39 On clustering tasks the model was able to work on a wide varieties of face images and is invariant to pose , lighting and also age. **Conclusion** * The model can be extended further to improve the overall accuracy. * Training networks to run on smaller systems like mobile phones. * There is need for improving the training efficiency. --- ## Notes * Harmonic embedding is a set of embedding that we get from different models but are compatible to each other. This helps to improve future upgrades and transitions to a newer model * To make the embeddings compatible with different models , harmonic-triplet loss and the generated triplets must be compatible with each other ## Open research questions * Better understanding of the error cases. * Making the model more compact for embedded and mobile use cases. * Methods to reduce the training times.
FaceNet: A Unified Embedding for Face Recognition and Clustering
Florian Schroff and Dmitry Kalenichenko and James Philbin
arXiv e-Print archive - 2015 via Local arXiv
Keywords: cs.CV


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