Abstract The current research on sketch face recognition focuses on transformation between photos and sketches, which reduces the modality gap between features extracted from photos and sketches. An approach is proposed to reduce the modality gap at the feature extraction stage. A face encoding method based on central error diffusion local binary pattern is used to capture the same face modality and reduce the difference between photos and sketches. Under the background that sketch recognition is actually the problem of single sample, the sample amount is extended by using wavelet packet decomposition and central error diffusion local binary pattern. Then, PCA+LDA is used to extract features and recognize faces. The experimental results indicate that the proposed algorithm reduces the modality gap between photos and sketches obviously and it has a higher recognition rate and better performance than the methods based on pseudo-sketches synthesis.
[1] Zhao W,Chellappa R,Phillips P J,et al.Face Recognition: A Literature Survey.ACM Computing Surveys,2003,35(4): 399-458 [2] Tang Xiaoou,Wang Xiaogang.Face Sketch Recognition.IEEE Trans on Circuits and Systems for Video Technology,2004,14(1): 50-57 [3] Liu Qingshan,Tang Xiaoou,Jin Hongliang,et al.A Nonlinear Approach for Face Sketch Synthesis and Recognition // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.San Diego,USA,2005: 1005-1010 [4] Wang Xiaogang,Tang Xiaoou.Face Photo-Sketch Synthesis and Recognition.IEEE Trans on Pattern Analysis and Machine Intelligence,2009,31(11): 1955-1967 [5] Gao Xinbo,Zhong Juanjuan,Li Jie,et al.Face Sketch Synthesis Algorithm Based on E-HMM and Selective Ensemble.IEEE Trans on Circuits and Systems for Video Technology,2008,18(4): 487-496 [6] Tan Xiaoyang,Chen Songcan,Zhou Zhihua,et al.Face Recognition from a Single Image per Person: A Survey.Pattern Recognition,2006,39(9): 1725-1745 [7] Ojala T,Pietikainen M,Harwood D.A Comparative Study of Texture Measures with Classification Based on Feature Distributions.Pattern Recognition,1996,29(1): 51-59 [8] Ojala T,Pietikainen M,Maenpaa T.Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns.IEEE Trans on Pattern Analysis and Machine Intelligence,2002,24(7): 971-987 [9] Garcia C,Zikos G,Tziritas G.Wavelet Packet Analysis for Face Recognition.Image and Vision Computing,2000,18(4): 289-297 [10] 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 [11] Chen Songcan,Liu Jun,Zhou Zhihua.Making FLDA Applicable to Face Recognition with One Sample per Person.Pattern Recognition,2004,37(7): 1553-1555
2012, Vol. 25 
