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

Xiaoyang Tan 1 Bill Triggs 2
1 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
2 AI - Artificial Intelligence
LJK - Laboratoire Jean Kuntzmann
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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Xiaoyang Tan, Bill Triggs. Fusing Gabor and LBP Feature Sets for Kernel-Based Face Recognition. AMFG - 3rd International Workshop Analysis and Modelling of Faces and Gestures, Oct 2007, Rio de Janeiro, Brazil. pp.235-249, ⟨10.1007/978-3-540-75690-3_18⟩. ⟨inria-00548672⟩

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