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Visual Speaker Authentication by a CNN-Based Scheme with Discriminative Segment Analysis

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Neural Information Processing (ICONIP 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1142))

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Abstract

Recent research shows that the static and dynamic features of a lip utterance contain abundant identity-related information. In this paper, a new deep convolutional neural network scheme is proposed. The entire lip utterance is first divided into a series of overlapping segments; then an adaptive scheme is designed to automatically examine the discriminative power and assign a corresponding weight of each segment in the entire utterance. The final authentication result of the entire utterance is determined by weighted voting of the results for all the segments. In addition, considering the various lighting condition in the natural environment, an illumination normalization procedure is proposed. Experimental results show that different segments of the same utterance have different discriminative power for user authentication, and focusing on the discriminative details will be more effective. The proposed method has shown superior performance compared with two state-of-the-art lip authentication approaches investigated.

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Acknowledgment

The work described in this paper is fully supported by NSFC Fund (No. 61771310).

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Authors and Affiliations

  1. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China

    Jiahui Sun, Shilin Wang & Quanhai Zhang

Authors
  1. Jiahui Sun
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  2. Shilin Wang
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  3. Quanhai Zhang
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Corresponding author

Correspondence to Shilin Wang .

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Editors and Affiliations

  1. Australian National University, Canberra, ACT, Australia

    Tom Gedeon

  2. Murdoch University, Murdoch, WA, Australia

    Kok Wai Wong

  3. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

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Sun, J., Wang, S., Zhang, Q. (2019). Visual Speaker Authentication by a CNN-Based Scheme with Discriminative Segment Analysis. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1142. Springer, Cham. https://doi.org/10.1007/978-3-030-36808-1_18

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  • DOI: https://doi.org/10.1007/978-3-030-36808-1_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36807-4

  • Online ISBN: 978-3-030-36808-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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