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Weighted Discriminant Analysis and Kernel Ridge Regression Metric Learning for Face Verification

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Book cover Neural Information Processing (ICONIP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9948))

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Abstract

A new formulation of metric learning is introduced by assimilating the kernel ridge regression (KRR) and weighted side-information linear discriminant analysis (WSILD) to enjoy the best of both worlds for unconstrained face verification task. To be specific, we formulate a doublet constrained metric learning problem by means of a second degree polynomial kernel function. The said metric learning problem can be solved analytically for Mahalanobis distance metric due to simplistic nature of KRR in which we named KRRML. In addition, the WSILD further enhances the learned Mahalanobis distance metric by leveraging the within-class and between-class scatter matrix of doublets. We evaluate the proposed method with Labeled Faces in the Wild database, a large benchmark dataset targeted for unconstrained face verification. The promising result attests the robustness and feasibility of the proposed method.

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Acknowledgments

The authors would like to thank Malaysia’s Fundamental Research Grant Scheme for supporting the research under grants MMUE/140026.

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Authors and Affiliations

  1. Faculty of Information Science and Technology, Multimedia University, Melaka, Malaysia

    Siew-Chin Chong & Thian-Song Ong

  2. School of Electrical and Electronic Engineering, Yonsei University, Seoul, South Korea

    Andrew Beng Jin Teoh

Authors
  1. Siew-Chin Chong
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  2. Andrew Beng Jin Teoh
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  3. Thian-Song Ong
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Corresponding author

Correspondence to Siew-Chin Chong .

Editor information

Editors and Affiliations

  1. The University of Tokyo, Tokyo, Japan

    Akira Hirose

  2. Kobe University, Kobe, Japan

    Seiichi Ozawa

  3. Okinawa Institute of Science and Technology Graduate University, Onna, Japan

    Kenji Doya

  4. Nara Institute of Science and Technology, Ikoma, Japan

    Kazushi Ikeda

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

    Minho Lee

  6. Chinese Academy of Sciences, Beijing, China

    Derong Liu

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Chong, SC., Teoh, A.B.J., Ong, TS. (2016). Weighted Discriminant Analysis and Kernel Ridge Regression Metric Learning for Face Verification. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9948. Springer, Cham. https://doi.org/10.1007/978-3-319-46672-9_45

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  • DOI: https://doi.org/10.1007/978-3-319-46672-9_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46671-2

  • Online ISBN: 978-3-319-46672-9

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