A Study Of Face Recognition Based On Ulti-Modal Fusion And Sparse Coding

With the development of society and advances in technology, the increasing demand of identification and authentication, common identification, face recognition, fingerprint recognition, iris recognition, voice recognition, face image with a high degree of recognition, accessibilityeasy and other advantages, has always been a hot research. For face recognition, in-depth discussion and analysis, and by a large number of comparative experiments clarify the advantages and disadvantages of the various methods, and through the integration and improvement of existing methods, we propose a more efficient recognition algorithm.This paper has done the following:1) Studied the common facial feature extraction algorithm, Gabor and LBP analysis of each algorithm, and experimental comparison, analyzes their advantages and defects. At the same time experimental comparison of different feature extraction methods, to draw and to determine the characteristics of the different extraction methods.2) PCA and LDA dimensionality reduction method discussed, through the comparison and analysis of the pros and cons of each algorithm and conditions.3) In-depth study of sparse coding principle, through a large number of experiments to verify its validity, and a detailed analysis of the deficiencies of the sparse coding.4) Summary and the algorithm from each other, the integration of these algorithms match each other. A lot of experiments and analysis of multi-feature fusion are tested for the validity of the algorithm.5) Based on multi-feature fusion and sparse coding classification face algorithm. By multi-feature block and weighted by the cascade of sparse coding classifier, the face object recognition. This algorithm has a higher recognition rate, and high robustness in the case of the presence of light and shade and cover, proving to be of high practical value.