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

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 ex...

• Type: Conference
• Year: 2007
• Where: AMFG
• Authors: Xiaoyang Tan, Bill Triggs
• Keywords: AMFG 2007, Biometrics, Discriminative Facial Appearance, Feature Sets, Practical Face Recognition