中心线邻域鉴别嵌入算法及其在人脸识别中的应用

Center-Based Line Neighborhood Discriminant Embedding Algorithm and Its Application to Face Recognition

针对边界费舍尔分析在特征提取过程中存在的不足,提出中心线邻域鉴别嵌入(CLNDE)算法,并应用于人脸识别中.CLNDE首先利用样本到类中心线的距离分别构造类内相似矩阵与类间相似矩阵;然后利用构造的相似矩阵计算样本的类间局部散度与类内局部散度;最后在最大化样本的类间局部散度的同时最小化类内局部散度,寻求最优投影矩阵.在人脸数据库上实验验证算法的优越性.

To overcome the drawbacks of the existing marginal fisher analysis algorithm in feature extraction, a center-based line neighborhood discriminant embedding （CLNDE） algorithm is proposed for face recognition. Firstly, the distance from a sample point to the center-based line is utilized to construct the within-class similarity matrix and the between-class similarity matrix, respectively. Next, the between-class local scatter and the within-class local scatter of samples are calculated by the constructed similarity matrices. Finally, the optimal transformation matrix is found by maximizing the between-class local scatter and minimizing the within-class local scatter simultaneously. Experimental results on face databases demonstrate the superiority of the proposed algorithm.