基于典型相关分析的组合特征抽取及脸像鉴别

利用典型相关分析的思想，提出了一种基于特征级融合的组合特征抽取新方法．首先，抽取同一模式的两组特征矢量，给出描述两组特征矢量之间相关性的判据准则函数；然后依此准则抽取它们的典型相关特征，构成有效鉴别特征矢量用于识别．该方法巧妙地将两组特征矢量之间的相关性特征作为有效判别信息，既达到了信息融合之目的，又消除了特征之间的信息冗余，为两组特征融合用于分类识别提供了新的思路．此外，从理论上进一步剖析了所提出的方法之所以能有效地用于识别的内在本质．在Yale和ORL标准人脸数据库上的实验结果证实了所提算法的有效性和稳定性，而且识别率大大高于用单一特征进行识别的结果．

Combined Feature Extraction Based on Canonical Correlation Analysis and Face Recognition

Feature level fusion plays an important role in the process of data fusion. According to the idea of canonical correlation analysis(CCA), a novel method of combined feature extraction is proposed in this paper. The main idea of this method can be described as follows. First of all, two groups of feature vectors with the same pattern sample are extracted, and the correlation criterion function between the two groups of feature vectors is established. Then, based on this criterion function, their canonical correlation features are extracted to form effective discriminant vectors for classification. The advantage of this method lies in the following aspects: firstly, it suits for information fusion; secondly, it eliminates the redundant information within the features, and a new way for classification and recognition is proposed. In addition, the essence of the efficiency is analyzed further in theory. The results of experiments on Yale and ORL face databases show that the recognition rate is far higher than that of the recognition adopting the single features, and that this algorithm is efficient and robust.