Local Bagging and Its Applicationon Face Recognition
Yulian Zhu · Transaction of Nanjing University of Aeronautics and Astronautics · 2010
Bagging is not quite suitable for stable classifiers such as nearest neighbor classifiers due to the lack of diversity and it is difficult to be directly applied to face recognition as well due to the small sample size (SSS) property of face recognition. To solve the two problems, local Bagging (L-Bagging) is proposed to simultaneously make Bagging apply to both nearest neighbor classifiers and face recognition. The major difference between L-Bagging and Bagging is that L-Bagging performs the bootstrap sampling on each local region partitioned from the original face image rather than the whole face image. Since the dimensionality of local region is usually far less than the number of samples and the component classifiers are constructed just in different local regions, L-Bagging deals with SSS problem and generates more diverse component classifiers. Experimental results on four standard face image databases (AR, Yale, ORL and Yale B) indicate that the proposed L-Bagging method is effective and robust to illumination, occlusion and slight pose variation.