Research On Face Recognition Based On Local Pattern

With the continuous development of modern computer vision technology and the continuous improvement of hardware computing capabilities,compared with other biometric recognition technologies such as fingerprint recognition,face recognition has many advantages,such as non-intrusive,high efficiency,friendliness and so on.it has been widely used in e-commerce,criminal identification,electronic payment,access control and other scenes,and has been concerned by a large number of researchers in recent years.Because the face recognition method based on local pattern mainly analyzes the local texture of the image and extracts local features,it shows a certain robustness to local changes such as expressions and lighting.However,the single feature representation of the current local pattern method is difficult to resist different environments,resulting in poor robustness to different usage scenarios.In addition,because the local pattern pays too much attention to the local information of a single image,only the features learned by the local pattern are limited.It is difficult to fully express the features of a face without integrating other features.Based on these considerations,this paper integrates local sampling,pattern coding optimization and multi-scale feature supplement to enhance the recognition effect of the algorithm.The main work is as follows:(1)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 direction 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 gray-scale spatial features of the extracted pixels to obtain dual 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 is obtained by fusing the features of double space.the recognition rate of the DVDSLDP method on the ORL and Yale databases are 99.5% 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.(2)In order to solve the problem that local binary pattern(LBP)and local graph structure(LGS)methods lack sufficient feature expression ability because of the unbalanced extraction method,and the limitation that only using local features can't fully describe face information,this paper proposes a method called Face recognition based on DOG multi-scale fusion of Balanced Local Pattern.Firstly,in view of the shortcomings of LBP and LGS,on the basis of balanced optimization by using double-circle cross-sampling,adaptive threshold based on local macro information,and a center-symmetric sampling graph structure,this paper proposes Extended Cross Local Binary Pattern(ECLBP)and Four-angle star Local Graph Structure(FLGS)methods with variable parameters,which are collectively called Balanced Local Pattern,which can enhance the extraction of key feature information.Then,the DOG pyramid generated by the Gaussian kernel and image difference is fused with the balanced local pattern method.The supplemented multi-scale feature map enriches the sample information while achieving the fusion of large-scale global contours and small-scale local details,which can further capture inter-class and intra-class difference characteristics.Finally,The comprehensive and accurate feature vector is obtained by weighted cascading the sub-block histograms of all feature map,and the nearest neighbor classifier is used to complete the recognition.The subjective and objective comparison of feature map and histogram theoretically verifies that the balanced local pattern can obtain local features with richer information and stronger discrimination.The proposed method is compared with the relevant typical methods,and the results on the ORL,AR and LFW databases show that the recognition rate of Balanced Local patterns is improved by up to 15.52% when the time consumption is the same as that of typical methods;after further integration of DOG pyramid,the recognition rate is increased by up to 9.24% again.The experimental results show that the balanced local pattern feature has stronger representation ability and robustness,and the supplementary multi-scale information further enhances the feature performance.especially when there are few training samples,the overall advantage of the algorithm is more obvious.In summary,the research on face recognition for local patterns shows that multi-spatial feature fusion can effectively enhance the use of key local features and improve the robustness of the recognition algorithm.In addition,the balanced optimization of local sampling and pattern coding can help the algorithm to capture more effective local information,and the supplementary multi-scale features can further make up for the limitation that the feature extraction of local pattern is too one-sided in describing face information,so as to effectively enhance the overall recognition performance of the algorithm.