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

摘要： 小样本问题和对局部变化（如遮挡、表情、光照等）识别的不鲁棒性是线性判别分析（LDA）在处理人脸图像时所常面临的问题。针对LDA的这些不足，提出了一种基于LDA的半随机子空间方法（SemiRS-LDA）。与传统的基于整个人脸样本特征集采样的随机子空间方法不同的是，SemiRS-LDA将随机采样建立在人脸图像的子图像上。该方法首先将人脸图像集划分成若干个子图像集，然后将随机子空间方法应用于每个子图像集上并构建多个LDA分类器，最后使用投票方法将各分类器进行组合。在两个标准人脸数据库（AR、ORL）上进行了实验，结果表明了所提方法不仅能获得较高的识别性能，而且对图像的光线、遮挡等也具有较强的鲁棒性。

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