Robust face recognition method based on dictionary decomposition and sparse representation

Abstract

The invention belongs to the field of pattern recognition, and particularly relates to a robust face recognition method based on dictionary decomposition and sparse representation. The method comprises the steps of designing a dictionary decomposition model to extract class specific information in a face image from a given face image training data set, then calculating a mapping matrix to describe a mapping relation between the class specific information and original training data, correcting the tested image according to the calculated mapping matrix, then reducing the dimensionality by using principal component analysis (PCA), and finally performing recognition classification via a sparse representation classifier (SRC). The method can effectively avoid the problem that the recognition rate is greatly reduced in the SRC recognition process because the training data is polluted or shaded or missing, and can achieve a high and stable recognition effect.

Description

technical field [0001] The invention belongs to the field of pattern recognition, in particular to a robust face recognition method based on dictionary decomposition and sparse representation. Background technique [0002] Face recognition technology refers to the input video stream or static image, after judging the presence of a face and further marking the position of the face, extracting the feature information of the face, comparing it with the pre-stored image database, and finally A technique for authenticating or identifying one or more individuals. With its wide range of practical applications, face recognition has become a hot topic in the field of computer vision and pattern recognition in recent years. Early face recognition algorithms first performed dimensionality reduction on face images (essentially face feature extraction), and then simply applied the nearest neighbor classifier (nearest neighbor classifier) ​​for classification. [0003] In 1991, Turk and...

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Problems solved by technology

But when the training samples are polluted, SRC does not have the desired effect. In 2011, the Robust Principal Component Analysis (RPCA) proposed by Candès, Li, Ma and Wright provides a method that can be used to solve this shortcoming. RPCA Decompose the training samples of each class into a low-rank matrix and the sum of a sparse matrix, and identify by reconstructing decontaminated images

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