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Margin Transfer: A Knowledge Distillation Method for Face Recognition to Recuperate Network Capacity

Face Recognition (FR) systems are one of the utmost used domains in real-world applications hence a field that needs network compression techniques habitually. Within this scope, we introduce a new knowledge distillation approach for FR to improve pruned model performance, which we call as Margin Transfer (MT). MT takes advantage of the margin penalty of the deeper baseline model in the training stage. We present an end-to-end trainable deep learning face recognition model that doesn't require any special software or hardware accelerators to achieve speedup. The experiments demonstrate and validate the performance of MT. Our method reduces more than 75% floating point operations per second (FLOPs) on ResNet-l00 with the same baseline accuracy. Moreover, MT reduces more than 61% FLOPs on ResNet-50 with accuracy improvement.