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Mirror PCA: Exploiting Facial Symmetry for Feature Extraction

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11643))

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Abstract

Feature extraction technique aiming at obtaining discriminative information from high-dimensional face images is of great importance in face recognition. One widely used method for extracting primary feature is Principal Component Analysis (PCA), which uses projection matrix for dimensionality reduction. There are many improvements of PCA but no one pays attention to the fact that both facial images and facial expression are symmetrical to some degree. Facial symmetry is a helpful characteristic, which benefits of feature extraction. In this paper, Mirror Principal Component Analysis (Mirror PCA) method is proposed for extracting representative facial features, which takes advantage of the facial symmetry in a face image. In order to verify the effectiveness of the proposed method, we compare the Mirror PCA method with other four methods on four famous face databases. The experimental results indicate that the representation capacity of our method is superior to others.

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Author information

Authors and Affiliations

  1. Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Jian-Xun Mi & Yueru Sun

  2. College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Jian-Xun Mi & Yueru Sun

Authors
  1. Jian-Xun Mi
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  2. Yueru Sun
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Correspondence to Jian-Xun Mi .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Polytechnic University of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

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Mi, JX., Sun, Y. (2019). Mirror PCA: Exploiting Facial Symmetry for Feature Extraction. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11643. Springer, Cham. https://doi.org/10.1007/978-3-030-26763-6_27

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  • DOI: https://doi.org/10.1007/978-3-030-26763-6_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26762-9

  • Online ISBN: 978-3-030-26763-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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