针对人脸识别中的小样本问题,本文提出了一种名为增强联系鉴别分析的方法并应用人脸识别中。该方法利用将人脸局部流形的结构信息和样本的类别信息进行有效地结合进行维数约简,首先构建人脸数据的近邻图与类别联系图,然后通过解决在一定约束条件下的优化问题来获取低维鉴别流形特征,实现在低维空间中同一类人脸数据聚集,不同类别间的人脸数据间尽可能发散,从而可以更好的应用于分类。在AT&T 和Yale 人脸图像数据库上的实验结果表明该方法能有效的提高人脸识别的性能。

Automatic face recognition is a challenging problem in the biometrics area, where the small sample size problem exists. An Enhanced Relation Discriminant Analysis (ERDA) method is proposed to solve the small sample size problem. In our framework, the neighbor and class relations of data are used to construct the embedding for classification problems. The proposed algorithm learns the embedding for the submanifold of each class by solving an optimization problem. After being embedded into a low-dimensional subspace, data points tend to move due to local intra-class attraction or inter-class repulsion. ERDA aims to map the image space into a submanifold that faithfully discovers the local discriminative manifold structure of face image. This method accounts for both the representation and the classification points of views. Experimental results on the AT&T and Yale face image databases demonstrate the effectiveness of the method.