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VIPLFaceNet: an open source deep face recognition SDK

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Abstract

Robust face representation is imperative to highly accurate face recognition. In this work, we propose an open source face recognition method with deep representation named as VIPLFaceNet, which is a 10-layer deep convolutional neural network with seven convolutional layers and three fully-connected layers. Compared with the well-known AlexNet, our VIPLFaceNet takes only 20% training time and 60% testing time, but achieves 40% drop in error rate on the real-world face recognition benchmark LFW. Our VIPLFaceNet achieves 98.60% mean accuracy on LFW using one single network. An open-source C++ SDK based on VIPLFaceNet is released under BSD license. The SDK takes about 150ms to process one face image in a single thread on an i7 desktop CPU. VIPLFaceNet provides a state-of-the-art start point for both academic and industrial face recognition applications.

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Acknowledgements

This work was partially supported by the National Basic Research Program of China (973 Program) (2015CB351802), and the National Natural Science Foundation of China (Grant Nos. 61402443, 61390511, 61379083, 61222211).

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Authors and Affiliations

  1. Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, 100190, China

    Xin Liu, Meina Kan, Wanglong Wu, Shiguang Shan & Xilin Chen

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Xin Liu, Meina Kan, Wanglong Wu, Shiguang Shan & Xilin Chen

Authors
  1. Xin Liu
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  2. Meina Kan
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  3. Wanglong Wu
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  4. Shiguang Shan
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  5. Xilin Chen
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Corresponding author

Correspondence to Shiguang Shan.

Additional information

Xin Liu recieved the BS degree from Chongqing University, China in 2011. Currently, he is a PhD candidate at the Institute of Computing Technology, Chinese Academy of Sciences, China. His research interests include face recognition, image retrieval, and deep learning.

Meina Kan is an associate professor with the Institute of Computing Technology, Chinese Academy of Sciences, China. She received the PhD degree from the University of Chinese Academy of Sciences, China. Her research mainly focuses on computer vision, especially face recognition, transfer learning, and deep learning.

Wanglong Wu recieved the BS degree from Beijing Jiaotong University, China in 2014. Currently, he is a PhD candidate at the Institute of Computing Technology, Chinese Academy of Sciences, China. His research interests include face recognition and deep learning.

Shiguang Shan received MS degree in computer science from Harbin Institute of Technology, China in 1999, and PhD degree in computer science from the Institute of Computing Technology (ICT), Chinese Academy of Sciences (CAS), China in 2004. He joined ICT, CAS in 2002 and has been a professor since 2010. He is now the deputy director of the Key Lab of Intelligent Information Processing of CAS. His research interests cover computer vision, pattern recognition, and machine learning. He especially focuses on face recognition related research topics. He has published more than 200 papers in refereed journals and proceedings.

Xilin Chen received the BS, MS, and PhD degrees in computer science from Harbin Institute of Technology, China in 1988, 1991, and 1994, respectively. He is now a professor with the Institute of Computing Technology, Chinese Academy of Sciences, China. He has authored one book and over 200 papers in refereed journals and proceedings in the areas of computer vision, pattern recognition, image processing, and multimodal interfaces.

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Liu, X., Kan, M., Wu, W. et al. VIPLFaceNet: an open source deep face recognition SDK. Front. Comput. Sci. 11, 208–218 (2017). https://doi.org/10.1007/s11704-016-6076-3

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