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MEFaceNets: Muti-scale Efficient CNNs for Real-Time Face Recognition on Embedded Devices

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Neural Information Processing (ICONIP 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1968))

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Abstract

The growing trend of applying face recognition technology on terminals and embedded devices highlights the critical need to strike a balance between recognition accuracy and real-time inference latency. In response to this challenge, we propose an efficient bottleneck named MEBottleneck, which utilizes convolution kernels of different sizes on two parallel branches to capture multi-scale features in the bottleneck, followed by a \(1 \times 1\) expansion layer to fuse multi-scale features, thereby improving the representation ability. Then, to balance the trade-off between accuracy and latency, we design a family of lightweight models with MEBottleneck, specifically tailored for face recognition and named MEFaceNets. Large kernels are used for depthwise convolutions in shallow layers, leading to notable improvements in accuracy. We evaluate the proposed models on several popular face recognition benchmarks. Our primary model achieves 99.80% face verification accuracy on LFW and exhibits excellent performance on the larger and more challenging benchmarks, including MegaFace Challenge 1, IJB-B and IJB-C. Furthermore, our proposed models have demonstrated impressive real-time performance on both the CPU and GPU of embedded devices.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grant No. 62172150).

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Authors and Affiliations

  1. College of Computer Science and Electronic Engineering, Hunan University, Changsha, China

    Jiazhi Li, Degui Xiao, Tao Lu & Shiping Dong

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  1. Jiazhi Li
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  2. Degui Xiao
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  3. Tao Lu
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  4. Shiping Dong
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Corresponding author

Correspondence to Degui Xiao .

Editor information

Editors and Affiliations

  1. Scholl of Automation, Central South University, Changsha, China

    Biao Luo

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Long Cheng

  3. Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China

    Zheng-Guang Wu

  4. School of Automation, Guangdong University of Technology, Guangzhou, China

    Hongyi Li

  5. School of Electrical Engineering and Telecommunications, UNSW Sydney, Sydney, NSW, Australia

    Chaojie Li

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Li, J., Xiao, D., Lu, T., Dong, S. (2024). MEFaceNets: Muti-scale Efficient CNNs for Real-Time Face Recognition on Embedded Devices. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1968. Springer, Singapore. https://doi.org/10.1007/978-981-99-8181-6_34

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  • DOI: https://doi.org/10.1007/978-981-99-8181-6_34

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