Method for identifying shielded face on basis of blocks and identification of non-negative matrix factorization

Abstract

The invention discloses a method for identifying a shielded face on basis of blocks and identification of non-negative matrix factorization. The method is adopted for solving the problem of low identifying rate for the large-area continuously shielded face of the prior art. According to the technical scheme, the method comprises the following steps: 1) constructing a data matrix and a No.k block non-negative matrix of a training dataset; 2) constructing a weight matrix and a new inter-category divergence matrix according to the cosine similarity between the categories of the data matrixes, thereby forming a new target function; 3) performing optimization solution on the target function, thereby acquiring a basis matrix and a coefficient matrix; 4) constructing a No.k block test data matrix of a test set, and projecting the No.k block test data matrix on the basis matrix, thereby acquiring a projecting coefficient matrix; 5) calculating distances from each column vector in the projecting coefficient matrix to all the column vectors in the coefficient matrix, and utilizing a weight fusion criterion to acquire the category of each image in the test set. An experiment proves that the identifying rate for the large-area continuously shielded face is increased according to the method provided by the invention and the method can be applied to the field of personal identification and information safety.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a face image recognition method, which can be applied to the fields of identification and information security. Background technique [0002] With the continuous advancement of science and technology, identification technology has a very important application status in many fields such as video surveillance, human-computer interaction, and access control. Compared with technologies such as ID cards and passwords with lower security, biometric identification technologies using human biological characteristics such as fingerprints and genes have the advantages of being safe, reliable, unique, and not easy to forge. Among all the biometric identification technologies, the use of facial features for identification is the most direct and convenient means, and has attracted the attention of many scholars. These unique advantages make face recognition, which extracts face feat...

AI Technical Summary

Problems solved by technology

Discriminative non-negative matrix factorization DNMF uses the discriminant information of sample data to construct divergence constraint items for the coefficient matrix, so that the samples can maintain the compact structure and inter-class discrimination of the data class in the low-dimensional space, and make good use of the data. Class information, but the discriminative non-negative matrix factorization DNMF has poor robustness and adaptability, especially for occluded face recognition in large continuous areas.

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