中心误差扩散局部二值模式下的草图人脸识别

摘要

目前的草图人脸识别主要集中在人脸照片-草图之间的相互转换,以此减少照片-草图特征之间的差异,从而进行识别.文中提出一种使用基于中心误差扩散局部二值模式的编码方法来获得具有相同模式的人脸形式,减小照片-草图之间的差异.在草图识别实际是单样本人脸识别的背景下,通过小波包分解和局部二值模式编码扩充样本数目.然后使用PCA+LDA来提取特征进行识别.实验结果表明,该算法可有效减小照片-草图之间的模式差异,且识别率和性能要优于之前的基于伪草图合成的方法.%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.