Research On Multi-pose Face Recognition Based On Random Forest And Sparse Representation

Face recognition is a very important research field in the field of computer vision and artificial intelligence.Face recognition has very high research significance and application value in human face-based disease screening and traffic intelligence monitoring combined with big data.Sparse representation theory is one of the research hotspots in recent years.Sparse representation has the advantages of strong robustness and accuracy in signal processing.The theory of sparse representation applied to face recognition has proved its feasibility theoretically.Experiments show that sparse representation of face recognition stability and efficiency.This thesis analyzes and researches deeply on face multi-posture appraisal problems,dimension-specific data dimensionality reduction problems,and robust sparse dictionary construction methods.The ultimate goal is to improve the robustness of the algorithm on multi-pose problems.The main research and innovations of this thesis are as follows:(1)The issue of multi-pose assessment was analyzed in detail.According to the relative position of the main facial organs and the relative geometric relationship,the pose eigenvectors are extracted and constructed,and the random forest algorithm is used to evaluate the face multi-pose.Finally,the proposed method is verified through experimental simulation.The main method of dimension reduction of face data is introduced.By analyzing the proportion of each pixel in the facial pose evaluation,a face data dimension reduction method is proposed of posture targeted.(2)To solve the problem of multi-pose face recognition,this thesis proposes a dual-dictionary idea(dictionaries divided into full-face dictionary and profile dictionary)to improve the robustness of the recognition algorithm in solving multi-pose problems.Combined with the result of multi-pose assessment,a pose-robust face recognition method based on random forest and dual-dictionary sparse representation is proposed.Compared with similar algorithms,the proposed method achieves better results in multi-pose robustness.The recognition rate in FERET face database increases by 9.90% on average.The recognition rate in the Pointing Data face database increased by an average of 1.90%.