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.
党力，孔凡让. 中心误差扩散局部二值模式下的草图人脸识别[J]. 模式识别与人工智能, 2012, 25(4): 699-704.
DANG Li, KONG Fan-Rang. Sketch Face Recognition Based on Central Error Diffusion Local Binary Pattern. , 2012, 25(4): 699-704.
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2012, Vol. 25 
