基于PCA余像空间的ICA混合特征人脸识别方法

Face recognition based on mixed feature of ICA in PCA residual face space

为改善传统的基于特征脸的人脸识别方法在识别光照变化较大的人脸时效果不尽理想的缺陷,提出一种基于“PCA余像空间”的ICA混合特征人脸识别方法。不同于2阶PCA人脸识别方法,用独立元分析法代替主元分析法,对“PCA余像特征脸集”进行独立元特征抽取得到人脸图像基于PCA余像空间的独立元特征,并综合人脸图像的原始独立元特征得到混合特征作为最终识别的特征。实验表明,基于PCA余像空间的ICA混合特征人脸识别方法,在识别光照、表情等外界因素变化较大的人脸图像时,要优于传统的基于特征脸的识别方法、基于ICA的识别方法以及基于2阶PCA的人脸识别方法,并具有较强的适用性。

To improve the effect of traditional eigenface method on face recognition under large illumination variation, a new face recognition method was proposed. Unlike second -order PCA face recognition, it used independent component analysis on the PCA residual eigenfaces instead of principal component analysis to extract the independent component feature, and integrated the IC feature in PCA residual face space with the IC feature in original face space to be the ultimate feature for recognition. Experiments prove that it is more efficient than some conventional human face recognition methods, such as eigenface based method, ICA based method, and second-order PCA method, under large illumination and pose variations, and also has a good practicability.