Face recognition method based on weighted diagnostic sparseness constraint nonnegative matrix decomposition

Abstract

The invention discloses a face recognition method based on weighted diagnostic sparseness constraint nonnegative matrix decomposition, and mainly aims to solve the problem that the method in the prior art is not robust to an obscured face and is of low recognition rate. According to the technical scheme, the method comprises the following steps: (1) constructing a nonnegative weight matrix according to the obscured area of a test image; (2) introducing the weight matrix into a general KL divergence objective function, applying a sparseness constraint to a basis matrix, and applying intra-class and inter-class divergence constraints to a coefficient matrix to get a weighted diagnostic sparseness constraint nonnegative matrix decomposition objective function; (3) solving the objective function, and decomposition-training a data matrix to get a basis matrix and a coefficient matrix; (4) projecting a test data matrix on the basis matrix to get a corresponding low-dimensional representation set, and taking the low-dimensional representation set as final test data; and (5) using a nearest neighbor classifier to classify the test data by taking the coefficient matrix as training data, and outputting the result. By using the method, the effect of obscured face recognition is improved. The method can be used in identity recognition and information security.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a method for extracting and identifying salient features of a human face image, which can be used for identity identification and information security. Background technique [0002] With the rapid development of the Internet, identity authentication technology has a very important application status in many fields such as e-commerce, human-computer interaction, public safety and network transmission. Compared with the traditional identification technology that combines information encryption and other strategies to add discriminative information to samples, biometric identification technologies such as fingerprints, irises, and voice that use image processing and pattern recognition to identify personal identities are unique and reliable. , convenience and not easy to be stolen and so on. Compared with other biological features, face features have the advantages of dire...

AI Technical Summary

Problems solved by technology

DNMF makes good use of the category information of the data, but its robustness and adaptability to face occlusion are poor, especially for occlusions with large continuous areas in the face image

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