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

By Mengjia Yan, Mengao Zhao, Zining Xu, Qian Zhang, Guoli Wang, Zhizhong Su. 2019

108 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Dacheng Tao
2. Jianping Gou
3. S. Maybank
4. Baosheng Yu
5. Dalong Du
6. Tao Mei
7. Jie Zhou
8. Xinze Chen

In Computer Vision and Pattern Recognition

1. Dacheng Tao
2. Xiaogang Wang
3. Luc Van Gool
4. Yi Yang
5. Jiwen Lu
6. Jie Zhou
7. Chen Change Loy
8. Trevor Darrell

Citations in the corpus

• Knowledge Distillation: A Survey (2020, methodology): “…al. 2019; Zhu et al. 2019) and then extended to other visual recognition applications, including face recognition (Luo et al. 2016; Kong et al. 2019; Yan et al. 2019; Ge et al. 2018; Wang et al. 2018b, 2019c; Duong et al. 2019; Wu et al. 2020; Wang et al. 2017; Zhang et al. 2020b; Wang et al.…”
• Exclusivity-Consistency Regularized Knowledge Distillation for Face Recognition (2020, background): “Yan et al. [52] employ the one-hot probability consistency to guide the network training and design a recursive knowledge distillation strategy to relieve the discrepancy between the teacher and student models.”
• WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition (2021, background): “Even though growing efforts have been devoted to investigating sophisticated networks [9, 80, 8, 71, 16] and losses [32, 68, 66, 12, 27, 55, 10], academia is restricted by limited training set and nearly saturated test protocols.”
• Review of Image Classification Algorithms Based on Convolutional Neural Networks (2021, methodology): “Later, VarGFaceNet [171] introduced Variable Group Convolution into the task of face recognition.”