子模式典型相关分析及其在人脸识别中的应用

传统的典型相关分析 (CCA) 是有效的特征提取方法之一, 已广泛应用于包括人脸识别在内的模式识别的许多领域. 但在人脸识别为代表的高维小样本问题上该方法存在如下不足: 1) 人脸识别的小样本特性使 CCA 两组特征矢量构成的总体协方差矩阵奇异, 难以直接应用; 2) 作为一种全局线性投影方法, 不足以很好地描述非线性的人脸识别问题; 3) 缺乏对局部变化的识别鲁棒性. 本文受已提出的子模式主分量分析 (SpPCA) 的启发, 提出了子模式典型相关分析 (SpCCA). 该方法将局部与全局特征矢量之间的相关性特征作为有效的判别信息, 既达到了融合局部与全局信息的目的, 又消除了特征之间的信息冗余. 通过子模式的划分, SpCCA 避免了小样本问题, 更好地描述了非线性的人脸识别问题; 并通过投票方式融合结果, 增强了对局部变化的鲁棒性. 在 AR 与 Yale 两个人脸数据集上的实验证实了该方法比对比方法不仅有更优的识别性能, 而且更加稳定和鲁棒.

Sub-pattern Canonical Correlation Analysis with Application in Face Recognition

Canonical correlation analysis(CCA)is a classic feature extraction method and is widely applied in pattern recognition.But in face recognition and other small sample size(SSS)problem,its typical disadvantages are:1)CCA fails,if directly applied,due to the singularity of the covariance matrices of its two groups of features caused by the SSS problem;2)it can not describe the nonlinear face recognition problem well,for its globally linear property in nature;3)it is short of the robustness to local variants.Enlightened by our previous sub-pattern PCA(SpPCA)we present sub-pattern canonical correlation analysis(SpCCA)in this paper.By maximizing the correlation between the local and global features of the original samples,this method can not only fuse local and global features well but also eliminate the redundant information among the features.By combining with the sub-pattern method,SpCCA avoids the SSS problem,realizes the formulation for the nonlinear face recognition problem better,and enhances the robustness to the local variants by voting.Experiments on AR and Yale face databases show that the proposed method is stable,robust,and effective.