Semi-random subspace LDA for face recognition
Yulian Zhu · Computer Engineering and Applications Journal · 2010
The small sample size(SSS) problem and the sensitivity to such variations as lighting,expression and occlusion are two challenging problems when LDA deals with the high dimensional face image.In order to address the two problems, this paper proposes a new method called as semi-random subspace LDA(SemiRS-LDA).Different from the traditional Random Subspace Method(RSM) which completely randomly samples features from the whole pattern feature set,SemiRS-LDA performs random sampling features on each local region(or a sub-image) partitioned from the original face image.More specifically,the paper first divides a face image into several sub-images in a deterministic way,then constructs a set of LDA classifiers on different random sampled feature set from each sub-images set,and finally combines all component classifiers for the final decision.Experiments on two benchmarks face databases(AR and ORL)show that the proposed SemiRS-LDA method is robust,effective in recognition performance.