Face Recognition Algorithm Based On Compressed Sensing Theory

With the development of computer technology,face recognition technology has gradually become a hot topic in computer vision and bioinformatics.Face recognition technology has been widely used in security verification,public security system and file management.The increase of the image data dimension and the limitation of the number of samples make the traditional face recognition technology more and more difficult in the concrete application.In order to solve the above problems,this paper studies the related face recognition based on the perceptual theory of compression,which can effectively solve the conversion of data from high dimension to low dimension,and avoid dimensionality disaster problem.The main contents of this paper are summarized as follows:Based on the sparse representation of the face recognition algorithm(SRC),the principal component analysis(PCA)algorithm is used to reduce the dimension.This method solves the problem that the length of the dictionary in the SRC algorithm is too long,and studies the correlation between the recognition run time and the recognition rate after the dimension reduction.This paper mainly studies how to improve the recognition rate in the case of occlusion,and proposes a SRC algorithm based on uniform block,which divides the face image into small pieces for training recognition.The subsequent study finds that the uniform block SRC algorithm can continue to be optimized The SRC algorithm based on the feature point block is proposed.The algorithm only extracts the feature points containing more information in the face image to carry out the training recognition.Considering the whole and the local relation,The SRC algorithm is combined with the SRC algorithm based on the feature point block,and the SRC secondary correction algorithm based on the feature point block is presented.Based on the KSVD algorithm,the learning algorithm of learning dictionary and the related research are put forward,and the sparse coefficient algorithm based on feature extraction matrix is proposed.The algorithm is used to calculate the projection matrix in the dictionary training stage,and then the sparse coefficients can be solved directly by the matrix,which avoids the process of solving the NP problem by using the iterative algorithm.