To overcome the effect of different illuminations and expressions on the recognition results of face images, a weighted multiscale and multiresolution face description and recognition method is presented. Multiresolution analysis is firstly employed to decompose a image into subimages, and three low frequence subbands with different scales are selected to construct multi-scale and multi-resolution image sequences. Then, the sign components of gray level difference between central pixel and its neighbors for each image in image sequences are encoded to express the importance of local face structure. Next, the magnitude components of gray level difference between central pixel and its neighbors are used as the weight of local binary pattern. Finally, block-based fisher linear discriminant analysis is utilized to reduce dimensions of the feature descriptor and enhance its discriminative ability. Experimental results on ORL and FERET face databases show that the proposed method gets significant performance improvement.
[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. Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711-720 [3] He X F, Yan S C, Hu Y X, et al. Face Recognition Using Laplacian Faces. IEEE Trans on Pattern Analysis and Machine Intelligence, 2005, 27(3): 328-340 [4] Baek K, Draper B A, Beveridge R, et al. PCA vs. ICA: A Comparison on the FERET Data Set // Proc of the Joint Conference on Information Sciences. Durham, USA, 2002: 824-827 [5] Liu C J, Wechsler H. Gabor Feature Based Classification Using the Enhanced Fisher Linear Discriminant Model for Face Recognition. IEEE Trans on Image Processing, 2002, 11(4): 467-476 [6] Ahonen T, Hadid A, Pietikinen M. Face Recognition with Local Binary Patterns // Proc of the 8th European Conference on Computer Vision. Prague, Czech Republic, 2004, I: 469-481 [7] Zhang W C, Shan S G, Gao W, et al. Local Gabor Binary Pattern Histogram Sequence (LGBPHS): A Novel Non-statistical Model for Face Representation and Recognition // Proc of the IEEE Interna- tional Conference on Computer Vision. Beijing, China, 2005, I: 786-791 [8] Zhang W C, Shan S G, Qing L Y, et al. Are Gabor Phases Really Useless for Face Recognition? Pattern Analysis and Applications, 2009, 12(3): 301-307 [9] Yuan B H, Wang H, Ren M W. Face Recognition Based on Completed Local Binary Pattern. Application Research of Computer, 2012, 29(4): 1557-1559 (in Chinese) (袁宝华,王 欢,任明武.基于完整LBP特征的人脸识别.计算机应用研究, 2012, 29 (4): 1557-1559) [10] Zhao H X, Xu F, Chen J Y. Face Recognition Based on Multi-scale LBP. Computer Application and Software, 2012, 29(1): 257-259, 279 (in Chinese) (赵怀勋,徐 锋,陈家勇.基于多尺度LBP的人脸识别.计算机应用与软件, 2012, 29 (1): 257-259, 279) [11] Zhang B C, Shan S G, Chen X L, et al. Histogram of Gabor Phase Patterns (HGPP): A Novel Object Representation Approach for Face Recognition. IEEE Trans on Image Processing, 2006, 16(1): 57-68 [12] Xie S F, Shan S G, Chen X L, et al. Fusing Local Patterns of Gabor Magnitude and Phase for Face Recognition. IEEE Trans on Image Processing, 2010, 19 (5): 1349-1361 [13] Yang M, Zhang L, Shiu S C K, et al. Monogenic Binary Coding: An Efficient Local Feature Extraction Approach to Face Recognition. IEEE Trans on Information Forensics and Security, 2012, 7(6): 1738-1751 [14] Ekenel H K, Gao H, Stiefelhagen R. 3D Face Recognition Using Local Appearance-Based Models. IEEE Trans on Information Forensics and Security, 2007, 2(3): 630-636 [15] Inan T, Halici U. 3D Face Recognition with Local Shape Descriptors.IEEE Trans on Information Forensics and Security, 2012, 7(2): 577-587 [16] Wright J, Yang A, Ganesh A, et al. Robust Face Recognition via Sparse Representation. IEEE Trans on Pattern Analysis and Machine Intelligence, 2009, 31(2): 210-227 [17] Yang M, Zhang D, Yang J, et al. Robust Sparse Coding for Face Recognition // Proc of the IEEE International Conference on Computer Vision and Pattern Recognition. Providence, USA, 2011: 625-632 [18] Elhamifar E, Vidal R. Robust Classification Using Structured Sparse Representation // Proc of the IEEE International Conference on Computer Vision and Pattern Recognition. Providence, USA, 2011: 1873-1879
2014, Vol. 27 
