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Remface: Study on Mini-sized Mobilenetv2 and Retinaface

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Artificial Intelligence and Security (ICAIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13339))

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Abstract

Nowadays, with the rapid development of mobile communication, big data and artificial intelligence technology, the optimization of face detection technology has become an important research direction in all fields. However, the existing face detection technology is not good enough to meet the requirements of all computing scenarios, and there are series of shortcomings, such as too many network parameters, longtime training and low detection success rate. In this paper, Widerface dataset published by the Chinese University of Hong Kong is used to study RetinaFace model and its effect. And the inverted residual structure is introduced by updating the main neural network, by loading which we work out RemFace at last. While adjusting the network structure, the neural degeneration phenomenon existing in previous studies is optimized, and the prediction effect is improved. Compared with MobileNetV1, RemFace has higher prediction accuracy and fewer model parameters, thus reducing computational overhead and enhancing real-time prediction. Finally, the paper summarizes the experimental results, and makes a simple prospect for the future research direction.

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Author information

Authors and Affiliations

  1. Chengdu University of Information Technology, Chengdu, China

    Zhengran Wang, Tao Wu & Yifan Wang

  2. University of Exeter, Exeter, USA

    Yuke Li

Authors
  1. Zhengran Wang
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  2. Tao Wu
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  3. Yifan Wang
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  4. Yuke Li
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Corresponding author

Correspondence to Tao Wu .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Xiaorui Zhang

  3. Jinan University, Guangzhou, China

    Zhihua Xia

  4. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Wang, Z., Wu, T., Wang, Y., Li, Y. (2022). Remface: Study on Mini-sized Mobilenetv2 and Retinaface. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2022. Lecture Notes in Computer Science, vol 13339. Springer, Cham. https://doi.org/10.1007/978-3-031-06788-4_1

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  • DOI: https://doi.org/10.1007/978-3-031-06788-4_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06787-7

  • Online ISBN: 978-3-031-06788-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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