VarGFaceNet: An Efficient Variable Group Convolutional Neural Network for Lightweight Face Recognition

11 Oct 2019  ·  Mengjia Yan, Mengao Zhao, Zining Xu, Qian Zhang, Guoli Wang, Zhizhong Su ·

To improve the discriminative and generalization ability of lightweight network for face recognition, we propose an efficient variable group convolutional network called VarGFaceNet. Variable group convolution is introduced by VarGNet to solve the conflict between small computational cost and the unbalance of computational intensity inside a block... We employ variable group convolution to design our network which can support large scale face identification while reduce computational cost and parameters. Specifically, we use a head setting to reserve essential information at the start of the network and propose a particular embedding setting to reduce parameters of fully-connected layer for embedding. To enhance interpretation ability, we employ an equivalence of angular distillation loss to guide our lightweight network and we apply recursive knowledge distillation to relieve the discrepancy between the teacher model and the student model. The champion of deepglint-light track of LFR (2019) challenge demonstrates the effectiveness of our model and approach. Implementation of VarGFaceNet will be released at https://github.com/zma-c-137/VarGFaceNet soon. read more

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Datasets


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Face Verification AgeDB-30 VarGFaceNet Accuracy 0.9815 # 2
Face Verification CFP-FP VarGFaceNet Accuracy 0.985 # 1
Face Verification Labeled Faces in the Wild VarGFaceNet Accuracy 99.85% # 1

Methods