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A Face Recognition Workflow Based Upon Similarity Measurement

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Book cover Biometric Recognition (CCBR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11818))

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Abstract

In order to combine with different feature extraction methods, in this paper, we propose a new method using similarity calculation between multiple features. We regard face recognition as a maximum-a-posteriori (MAP) problem and the de-pendency between different features is defined by a markov chain. We construct a matching similarity function T which helps us finding a better matching image. Experiments were tested using AR database and the results have shown that our recognition rate is higher, especially robust to small occlusion and noise.

Yigan Li, master student main research interests: image processing face recognition.

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Author information

Authors and Affiliations

  1. Hainan University, Haikou, Hainan Province, China

    Yigan Li & Zhaohui Wang

Authors
  1. Yigan Li
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  2. Zhaohui Wang
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Correspondence to Yigan Li .

Editor information

Editors and Affiliations

  1. Chinese Academy of Sciences, Beijing, China

    Zhenan Sun

  2. Chinese Academy of Sciences, Beijing, China

    Ran He

  3. Tsinghua University, Beijing, China

    Jianjiang Feng

  4. Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

  5. Tsinghua University, Shenzhen, China

    Zhenhua Guo

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Li, Y., Wang, Z. (2019). A Face Recognition Workflow Based Upon Similarity Measurement. In: Sun, Z., He, R., Feng, J., Shan, S., Guo, Z. (eds) Biometric Recognition. CCBR 2019. Lecture Notes in Computer Science(), vol 11818. Springer, Cham. https://doi.org/10.1007/978-3-030-31456-9_31

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  • DOI: https://doi.org/10.1007/978-3-030-31456-9_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31455-2

  • Online ISBN: 978-3-030-31456-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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