Face recognition method based on discriminative non-convex low-rank decomposition and superposition linear sparse representation

Abstract

The invention discloses a face recognition method based on non-convex low-rank decomposition and superposition linear sparse representation. The method comprises the following steps: 1, according to alow-rank matrix decomposition theory, replacing a nuclear norm with a gamma norm for low-rank matrix decomposition, and introducing a structure incoherent discrimination item to form discriminative non-convex low-rank decomposition; 2, resolving the discriminative non-convex low-rank decomposition, and decomposing the face sample matrix into a low-rank matrix and a sparse matrix; 3, decomposing the low-rank matrix into prototype dictionaries and variation dictionaries through superposition linear representation, and then using the two dictionaries as dictionaries for testing through linear weighting combination; and 4, solving a sparse coefficient of l1 norm by using a homotopy method by using a sparse representation algorithm, carrying out classified recognition on the face images by reconstructing a minimum sparse residual model, and classifying the face samples to be tested into a class with a minimum error, thereby realizing face recognition. According to the method, good robustness and high efficiency can be maintained under the conditions of shielding and noise pollution.

Description

Technical field [0001] The invention belongs to the technical field of biometric identification and information security, and particularly relates to a face recognition method based on low-rank decomposition and sparse representation. Background technique [0002] Face recognition is an important topic in pattern recognition in the past two decades. It includes computer vision, pattern recognition and many other subjects, and it also has a very wide range of applications. As the application range becomes wider and wider, face recognition is being valued by more and more important national institutions and enterprises. Now, face recognition has shown its application value in many fields such as related documents and various financial card identity verification, various security verification systems. [0003] Face recognition is to use a computer to find a face image in a single static face image or dynamic video, and then use related algorithms to extract feature information that i...

AI Technical Summary

Problems solved by technology

[0004] The face recognition algorithm based on Sparse Representation (SRC) forms an over-complete dictionary according to the training samples, so as to establish a linear expression for the test samples, and complete the classification according to the minimum voting criterion; Partially occluded face images have a certain degree of robustness; however, training samples are always insufficient, and the actual dictionary obtained is not complete

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