Unconstrained face recognition method based on weighted block tensor sparse graph mapping

Abstract

The invention discloses an unconstrained face recognition method based on weighted block tensor sparse graph mapping, and relates to the technical field of face recognition methods. Firstly, an original sample image is divided into B blocks, each image block is represented by a second-order tensor, a category label is introduced, and a supervised over-complete block tensor dictionary is constructed; on the basis of solving similar sparse reconstruction coefficients of the block samples through regular constraints, intra-class compactness constraints and weight constraints are added, intra-class differences between the similar block samples are further represented through distance weights, and a more accurate sparse neighbor graph is constructed in a block sample tensor space in a self-adaptive mode; and finally, bilateral low-dimensional projection is performed on the block samples, and a global constraint factor is introduced to obtain a bilateral low-dimensional projection matrix. According to the method, the complexity of the non-constrained face image data is fully considered, the neighbor distribution graph of the high-dimensional tensor data is obtained in a self-adaptive mode through the sparse representation technology, the low-dimensional manifold essential structure of the highly-distorted non-constrained face data is effectively extracted, and the accuracy of non-constrained face recognition is greatly improved.

Description

technical field [0001] The present invention relates to the technical field of face recognition methods, in particular to the technical field of unconstrained face recognition methods based on weighted block tensor sparse graph mapping. Background technique [0002] With the rapid development of mobile Internet, electronic sensing technology, and machine learning theory, real-time collection of face images for identity authentication, video surveillance and human-computer interaction has become an important application of artificial intelligence in actual work and life. Due to the mixed interference of various factors such as illumination, posture, expression, occlusion, age, and resolution, the face data collected in the real environment leads to the diversity of face images and a highly complex nonlinear distribution in high-dimensional space. . Therefore, how to effectively reduce the dimensionality of high-dimensional massive unconstrained face data is particularly impo...

AI Technical Summary

Problems solved by technology

However, the distribution of real data in practical applications is complex and unknown

Taking unconstrained face recognition as an example, the face images collected in the real environment, due to factors such as expression, occlusion, illumination, age, etc., will show great differences between similar samples, and sometimes heterogeneous samples cannot. fully differentiated

Therefore, it is very difficult to define an accurate neighbor graph in advance, and the neighbor relationship between samples described by the distance measure will also weaken as the dimension increases, which to some extent inhibits the multidimensional projection technology based on tensor representation in the real world. , Wide application in complex data

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