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Fusing Gabor and LBP Feature Sets for Kernel-Based Face Recognition

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Analysis and Modeling of Faces and Gestures (AMFG 2007)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4778))

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Abstract

Extending recognition to uncontrolled situations is a key challenge for practical face recognition systems. Finding efficient and discriminative facial appearance descriptors is crucial for this. Most existing approaches use features of just one type. Here we argue that robust recognition requires several different kinds of appearance information to be taken into account, suggesting the use of heterogeneous feature sets. We show that combining two of the most successful local face representations, Gabor wavelets and Local Binary Patterns (LBP), gives considerably better performance than either alone: they are complimentary in the sense that LBP captures small appearance details while Gabor features encode facial shape over a broader range of scales. Both feature sets are high dimensional so it is beneficial to use PCA to reduce the dimensionality prior to normalization and integration. The Kernel Discriminative Common Vector method is then applied to the combined feature vector to extract discriminant nonlinear features for recognition. The method is evaluated on several challenging face datasets including FRGC 1.0.4, FRGC 2.0.4 and FERET, with promising results.

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Authors and Affiliations

  1. INRIA and Laboratoire Jean Kuntzmann, 655 avenue de l’Europe, Montbonnot 38330, France

    Xiaoyang Tan & Bill Triggs

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  1. Xiaoyang Tan
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  2. Bill Triggs
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Editor information

S. Kevin Zhou Wenyi Zhao Xiaoou Tang Shaogang Gong

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© 2007 Springer-Verlag Berlin Heidelberg

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Tan, X., Triggs, B. (2007). Fusing Gabor and LBP Feature Sets for Kernel-Based Face Recognition. In: Zhou, S.K., Zhao, W., Tang, X., Gong, S. (eds) Analysis and Modeling of Faces and Gestures. AMFG 2007. Lecture Notes in Computer Science, vol 4778. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75690-3_18

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  • DOI: https://doi.org/10.1007/978-3-540-75690-3_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-75689-7

  • Online ISBN: 978-3-540-75690-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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