Face Recognition Based On Improved Nearest Feature Space Embedding Method

Face recognition is an important research field in pattern recognition and belongs to the category of biometrics.Face recognition technology takes advantage of the fact that different human facial features have many differences,and completes identification by locating the face images appearing in the video or image,processing them and matching them with the images in the existing face library one by one.After decades of development,face recognition technology has become increasingly mature.Facial images is convenient to obtain,low cost and easy to be accepted by people,so the face recognition system is widely used in many occasions to improve the efficiency and accuracy of identification.This article introduces some classic face recognition algorithms,and discusses their advantages and disadvantages,as well as their development direction.1)In this study,nearest feature space embedding method based on the combination of nonlinear distance metric and included angle(NL-IANFSE)is proposed to overcome the above drawback.In the training phase,NL-IANFSE brings in nonlinear distance measure to make the rate of change of within-class scatter much slower than that of between-class scatter so that distances of samples within same class will be smaller and distances of samples belong to different classes will be larger in the transformed space.In the matching phase,NL-IANFSE uses the nearest neighbor classifier that combines Euclidean distance and included angle between two samples,taking the relationship between similarity of samples and included angles of samples into account,which is more suited to classify in higher space.According to the experimental results,the proposed method outperforms the other algorithms for classification in high dimensional space.2)Nearest feature space embedding method based on nonlinear distance metric(NDNFSE)was developed by using nonlinear distance formula to select the nearest feature spaces and using the nearest neighbor classifier combines Euclidean distance and included angle between two samples to improve the recognition rate.NDNFSE also sought every class' nearest classes firstly,then only selected a sample's nearest feature spaces within them to save the training time.According to the experimental results,NDNFSE outperforms comparison algorithms for classification as a whole,with a much shorter training time than that of NFSE.