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Face Detection Using Improved Multi-task Cascaded Convolutional Networks

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Security, Privacy, and Anonymity in Computation, Communication, and Storage (SpaCCS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12383))

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Abstract

Compared with traditional face detection methods, deep learning methods can better deal with face detection in unconstrained environment. In this paper, based on the original Multi-Task Cascaded Convolutional Networks (MTCNN), we make corresponding improvements on it. On the premise of ensuring that the accuracy of the improved algorithm for face detection is similar to the original, we improve the detection speed. First of all, the original network structure is optimized. In order to improve the detection speed, the amount of computation should be reduced as much as possible. Therefore, it is proposed to use Depthwise convolution in MobileNet instead of conventional convolution; in addition, median filter is used before image detection to reduce some noise and improve the performance of the algorithm. After training on the Wider Face and Celeba data set, the test images were tested. Experimental results show that the performance of the improved MTCNN is not significantly reduced, and the detection speed is significantly improved.

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Authors and Affiliations

  1. School of Electronics and Communication, Heilongjiang University of Science and Technology, Harbin, 150022, China

    Xiaolin Jiang & Yu Xiang

Authors
  1. Xiaolin Jiang
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  2. Yu Xiang
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Corresponding author

Correspondence to Xiaolin Jiang .

Editor information

Editors and Affiliations

  1. Guangzhou University, Guangzhou, China

    Guojun Wang

  2. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Bing Chen

  3. Computer Science, Georgia State University, Atlanta, GA, USA

    Wei Li

  4. College of Science and Engineering, Qatar Foundation Education City, Doha, Qatar

    Roberto Di Pietro

  5. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Xuefeng Yan

  6. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Hao Han

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Jiang, X., Xiang, Y. (2021). Face Detection Using Improved Multi-task Cascaded Convolutional Networks. In: Wang, G., Chen, B., Li, W., Di Pietro, R., Yan, X., Han, H. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2020. Lecture Notes in Computer Science(), vol 12383. Springer, Cham. https://doi.org/10.1007/978-3-030-68884-4_27

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  • DOI: https://doi.org/10.1007/978-3-030-68884-4_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-68883-7

  • Online ISBN: 978-3-030-68884-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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