Sparse reinforcement type low-rank constraint face image clustering method

Abstract

The invention relates to a sparse reinforcement type low-rank constraint face image clustering method, which aims to effectively cluster shielded, illuminated and expression-changed face images and obtain a relatively high recognition rate, and comprises the following steps of: (1) carrying out weighted feature evaluation of sparse representation on the images to ensure that reconstruction representation is implemented through effective features; (2) using a regularized non-convex penalty function to constrain the representation coefficient matrix, and giving a parameter norm to keep the convexity of the overall objective function; (3) establishing an FERP clustering model by taking the image matrix X as input and taking the feature weight vector p and the reconstruction representation coefficient matrix Z as variables to be optimized; (4) solving the problem of Z and p double-variable optimization by using an augmented Lagrangian function and an alternating direction multiplier method; (5) calculating an incidence matrix S by using a formula (|Z<*>|+| Z<*T>|) / 2; and (6) segmenting the incidence matrix S by using an Ncut segmentation algorithm to obtain a clustering image. The method has the advantages of high operation efficiency, high data adaptability, high accuracy and high expansibility, and is suitable for face image clustering and recognition.

Description

technical field [0001] The invention is a face image clustering method, in particular, a sparse enhanced low-rank constrained face image clustering method, which can be used for image clustering, target recognition and the like. Background technique [0002] Face recognition is one of the most challenging problems in computer vision and pattern recognition research. In recent years, face recognition has been widely used in access control systems, identification, online payment, digital entertainment, etc. Face recognition is essentially a biometric technology for identification based on human facial feature information. Everyone's face is composed of forehead, eyebrows, glasses, nose, mouth, cheeks and other parts, and the approximate positional relationship between them is also fixed. Based on this information, further extracting the identity feature data contained in each face is an important way of identity identification. [0003] Compared with other traditional biome...

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Problems solved by technology

[0006] The present invention aims to solve the problems that the existing face image clustering methods are severely affected by illumination, occlusion, and expression changes in face images, low local information discrimination, invalid feature interference, and high computational complexity, and provides a sparse enhancement A face image clustering method with low-rank constraints, which can be used for image clustering and target recognition

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