双偏差双空间局部方向模式的人脸识别

针对局部方向数(Local Directional Number pattern,LDN)类方法的人脸识别通常仅利用梯度信息且信息提取不充分的问题,提出双偏差双空间局部方向模式(Double Variation and Double Space Local Directional Pattern,DVDSLDP).该方...

Face Recognition Based on Double Variation and Double Space Local Directional Pattern

In order to solve the problem that face recognition based on Local Directional Number pattern（LDN） usually only uses gradient information and does not extract enough information, a method called Double Variation and Double Space Local Directional Pattern（DVDSLDP） is proposed. Firstly, this method expands the associated neighborhood information by pixel sampling, and then the relative deviation and absolute deviation are obtained by edge response operator and local forward and backward difference respectively to form double deviation information, which can fully extract the information of the local gradient space. Then the gradient spatial features are cascaded with the grayscale spatial features of the extracted pixels to obtain double spatial features, which are used for pattern coding to get the feature image. Finally, the face feature vector is obtained by weighted cascading the sub-block histograms according to the information entropy, and the nearest neighbor classifier is used to complete the classification. The proposed method is compared with the relevant typical methods, and the results on the ORL, Yale and AR databases show that the feature images with clearer outline and richer texture are obtained by fusing the features of double space. The recognition rate of the DVDSLDP method on the ORL and Yale databases are 99.50% and 94.44%, respectively, especially when there are few training samples, the performance of the proposed method is significantly improved. Meanwhile, in particular, it is worth mentioning that the recognition rate of the proposed method on the AR expression, illumination, occlusion A and occlusion B databases are 99.67%, 100%, 99.33% and 97.33%, respectively, which is significantly higher than other methods, the proposed method shows good robustness.