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In recent years, the performance of face verification and recognition systemsbased on deep convolutional neural networks (DCNNs) has significantly improved.A typical pipeline for face verification includes training a deep network forsubject classification with softmax loss, using the penultimate layer output asthe feature descriptor, and generating a cosine similarity score given a pairof face images or videos. The softmax loss function does not optimize thefeatures to have higher similarity score for positive pairs and lowersimilarity score for negative pairs, which leads to a performance gap. In thispaper, we propose a new loss function, called Crystal Loss, that restricts thefeatures to lie on a hypersphere of a fixed radius. The loss can be easilyimplemented using existing deep learning frameworks. We show that integratingthis simple step in the training pipeline significantly improves theperformance of face verification and recognition systems. We achievestate-of-the-art performance for face verification and recognition onchallenging LFW, IJB-A, IJB-B and IJB-C datasets over a large range of falsealarm rates (10-1 to 10-7).

Crystal Loss and Quality Pooling for Unconstrained Face Verification and Recognition