基于判别改进局部切空间排列特征融合的人脸识别方法

改进型局部切空间排列(ILTSA)是最近提出的一种流形学习方法。基于对ILTSA的线性逼近和判别拓展，该文提出一种新的称为判别改进局部切空间排列(DILTSA)的特征提取方法，并给出了理论证明和算法分析。基于最大邻域间隔准则和ILTSA, DILTSA能够同时保持类内与类间局部判别几何结构。此外，提出一种增强型Gabor-like复数小波变换以缓解照明和表情变化对人脸识别的影响。通过融合Gabor-like复数小波变换和原始图像特征，能够进一步提高人脸识别的准确率。在Yale 和PIE人脸数据库上的实验结果证明了所提方法的有效性。

Discriminant Improved Local Tangent Space Alignment Feature Fusion for Face Recognition

Improved Local Tangent Space Alignment (ILTSA) is a recent manifold learning method. In this paper, based on linearization and discriminant extension of ILTSA, a novel feature extraction method named Discriminant ILTSA (DILTSA) is proposed with its theory and algorithm analysis. Based on maximum neighborhood margin criterion and ILTSA, DILTSA can preserve both local within-class and between-class geometry structures. In face recognition application, an augmented Gabor-like complex wavelet transform is proposed, which can efficiently alleviate the illumination and expression variation effect. An approach for face recognition based on the fusion of local and holistic features is developed. Experimental results on Yale and PIE face databases demonstrate the effectiveness of the proposed face recognition method.