关键词: 线性判别分析（LDA）, 人脸识别, 半随机子空间, 小样本问题

Abstract: 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.

Key words: Linear Discriminant Analysis（LDA）, face recognition, Semi-Random Subspace（Semi-RS）, small sample size problem