Center-Based Line Neighborhood Discriminant Embedding Algorithm and Its Application to Face Recognition
YANG Zhang-Jing1, HUANG Pu2, ZHANG Fan-Long1, YANG Guo-Wei1
1.School of Technology, Nanjing Audit University, Nanjing 211815 2.School of Computer Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023
Abstract：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.
杨章静，黄璞，张凡龙，杨国为. 中心线邻域鉴别嵌入算法及其在人脸识别中的应用*[J]. 模式识别与人工智能, 2015, 28(12): 1100-1109.
YANG Zhang-Jing , HUANG Pu , ZHANG Fan-Long , YANG Guo-Wei. Center-Based Line Neighborhood Discriminant Embedding Algorithm and Its Application to Face Recognition. , 2015, 28(12): 1100-1109.
[1] Turk M, Pentland A. Eigenfaces for Recognition. Journal of Cognitive Neuroscience, 1991, 3(1): 71-86 [2] Belhumeur P N, Hespanha J P, Kriegman D J. Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711-720 [3] Martinez A M, Kak A C. PCA versus LDA. IEEE Trans on Pattern Analysis and Machine Intelligence, 2001, 23(2): 228-233 [4] Tenenbaum J B, de Silva V, Langford J C. A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science, 2000, 290(5500): 2319-2323 [5] Roweis S T, Saul L K. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science, 2000, 290(5500): 2323-2326 [6] Belkin M, Niyogi P. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation. Neural Computation, 2003, 15(6): 1373-1396 [7] He X F, Yan S C, Hu Y X, et al. Face Recognition Using Laplacianfaces. IEEE Trans on Pattern Analysis and Machine Intelligence, 2005, 27(3): 328-340 [8] Zheng Z L, Huang X Q, Jia J, et al. Locality Preserving Projection with Sparse Penalty. Chinese Journal of Computers, 2014, 37(9): 2038-2046 (in Chinese) (郑忠龙,黄小巧,贾,等.稀疏局部保持投影.计算机学报, 2014, 37(9): 2038-2046) [9] Wan J W, Yang M, Ji G L, et al. Weighted Cost Sensitive Locality Preserving Projection for Face Recognition. Journal of Software, 2013, 24(5): 1155-1164 (in Chinese) (万建武,杨 明,吉根林,等.一种面向人脸识别的加权代价敏感局部保持投影.软件学报, 2013, 24(5): 1155-1164) [10] Yang J, Zhang D, Yang J Y, et al. Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Applications to Face and Palm Biometrics. IEEE Trans on Pattern Analysis and Machine Intelligence, 2007, 29(4): 650-664 [11] Yan S C, Xu D, Zhang B Y, et al. Graph Embedding and Extensions: A General Framework for Dimensionality Reduction. IEEE Trans on Pattern Analysis and Machine Intelligence, 2007, 29(1): 40-51 [12] Sun Z J, Xue L, Xu Y M. Marginal Fisher Feature Extraction Algorithm Based on Deep Learning. Journal of Electronics & Information Technology, 2013, 35(4): 805-811(in Chinese) (孙志军,薛 磊,许阳明.基于深度学习的边际Fisher分析特征提取算法.电子与信息学报, 2013, 35(4): 805-811) [13] Huang P, Chen C K, Tang Z M, et al. Feature Extraction Using Local Structure Preserving Discriminant Analysis. Neurocompu-ting, 2014, 140: 104-113 [14] Wan M H, Li M, Yang G W, et al. Feature Extraction Using Two-Dimensional Maximum Embedding Difference. Information Sciences, 2014, 274: 55-69 [15] Lai Z H, Li Y J, Wan M H, et al. Local Sparse Representation Projections for Face Recognition. Neural Computation & Application, 2013, 23(7): 2231-2239 [16] Xu J, Yang J, Lai Z H. K-Local Hyperplane Distance Nearest Neighbor Classifier Oriented Local Discriminant Analysis. Information Sciences, 2013, 232: 11-26 [17] Huang P, Tang Z M, Chen C K, et al. Local Maximal Marginal Discriminant Embedding for Face Recognition. Journal of Visual Communication and Image Representation, 2014, 25(2): 296-305 [18] Huang P, Chen C K, Tang Z M, et al. Discriminant Similarity and Variance Preserving Projection for Feature Extraction. Neurocomputing, 2014, 139: 180-188 [19] Yang W K, Sun C Y, Zhang L. A Multi-manifold Discriminant Analysis Method for Image Feature Extraction. Pattern Recognition, 2011, 44(8): 1649-1657 [20] Gao Q B, Wang Z Z. Center-Based Nearest Neighbor Classifier. Pattern Recognition, 2007, 40(1): 346-349
2015, Vol. 28 
