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An Efficient Pose Tolerant Face Recognition Approach

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Transactions on Computational Science XXVI

Part of the book series: Lecture Notes in Computer Science ((TCOMPUTATSCIE,volume 9550))

Abstract

Face recognition is biometric pattern recognition, which is more acceptable and convenient for users compared with other biometric recognition traits. Among many problems in face recognition system, pose problem is considered as one of the major problem still unsolved in satisfactory level. This paper proposes a novel pose tolerant face recognition approach which includes feature extraction, pose transformation learning and recognition stages. In the first stage, 2DPCA is used as robust feature extraction technique. The linear regression is used as efficient and accurate transformation learning technique to create frontal face image from different posed face images in the second stage. In the last stage, Mahalanobis distance is used for recognition. Experiments on FERET and FEI face databases demonstrated the higher performance in comparison with traditional systems.

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Authors and Affiliations

  1. Department of Computer Engineering, Ankara University, Ankara, Turkey

    Refik Samet, Ghulam Sakhi Shokouh & Kemal Batuhan Baskurt

Authors
  1. Refik Samet
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  2. Ghulam Sakhi Shokouh
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  3. Kemal Batuhan Baskurt
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Corresponding author

Correspondence to Refik Samet .

Editor information

Editors and Affiliations

  1. University of Calgary, Calgary, Alberta, Canada

    Marina L. Gavrilova

  2. Sardina Systems OÜ, Tallinn, Estonia

    C.J. Kenneth Tan

  3. University of Cantabria, Santander, Spain

    Andrés Iglesias

  4. Toho University, Tokyo, Japan

    Mikio Shinya

  5. University of Cantabria, Santander, Spain

    Akemi Galvez

  6. Nanyang Technological University, Singapore, Singapore

    Alexei Sourin

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© 2016 Springer-Verlag Berlin Heidelberg

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Samet, R., Shokouh, G.S., Baskurt, K.B. (2016). An Efficient Pose Tolerant Face Recognition Approach. In: Gavrilova, M., Tan, C., Iglesias, A., Shinya, M., Galvez, A., Sourin, A. (eds) Transactions on Computational Science XXVI. Lecture Notes in Computer Science(), vol 9550. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49247-5_10

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  • DOI: https://doi.org/10.1007/978-3-662-49247-5_10

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-49246-8

  • Online ISBN: 978-3-662-49247-5

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