A Face Recognition Method Based on Weighted Huber Constrained Sparse Coding

A sparse coding, face recognition technology, applied in character and pattern recognition, instruments, computing, etc., can solve problems such as difficulty in distinguishing individuals, and achieve the effect of avoiding inter-class interference, expanding effects, and increasing inter-class changes

Active Publication Date: 2022-01-28
CHONGQING UNIV OF POSTS & TELECOMM
View PDF4 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

For faces, the interference of intra-class changes is often greater than that of inter-class changes, so it becomes extremely difficult to use inter-class changes to distinguish individuals under the interference of intra-class changes

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A Face Recognition Method Based on Weighted Huber Constrained Sparse Coding
  • A Face Recognition Method Based on Weighted Huber Constrained Sparse Coding
  • A Face Recognition Method Based on Weighted Huber Constrained Sparse Coding

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0119] This embodiment provides a face recognition method based on weighted Huber constrained sparse coding, Figure 15 shown, including:

[0120]S101. Using a regression classifier as the basis of face recognition, introducing L1 regular constraints, and sparsely encoding coefficients of query samples in training samples to obtain a sparse coding model;

[0121] The general framework based on regression classifiers is explained as follows:

[0122] In general classification problems, training samples are expressed as a dictionary matrix X=[X 1 , X 2 ,...,X c ]∈R m×n ;c is the sample category; is the sample subset of each category of the sample set X; n i is the number of training samples of class i, is the total number of samples. In regression, the training sample X linearly represents the query sample y:

[0123]

[0124] in is the encoding coefficient of the query sample y to be determined on the training sample X.

[0125] Regression-based classification ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The present invention provides a face recognition method based on weighted Huber constraint sparse coding, comprising: using a regression classifier as the basis of face recognition, introducing L1 regular constraints, and sparsely encoding coefficients of query samples in the training sample set X The sparse coding model is obtained; on the basis of the sparse coding model, the Huber loss function is used to replace the L1 fidelity item or the L2 fidelity item to obtain a sparse robust coding model; according to the training sample set and the residual of the query sample, the training The weight of each pixel in the sample set; on the basis of the sparse robust coding model, use the weight and the threshold of the Huber loss function to obtain a weighted Huber constrained sparse coding model; obtain the query sample in the training sample set X according to its coding coefficient The residual vector; analyze the recognition rate of the query sample in the occlusion environment according to the residual vector. The invention effectively reduces intra-class variation, avoids inter-class interference, expands the effect of weight vectors, and improves the recognition rate.

Description

technical field [0001] The invention relates to the technical field of face recognition, in particular to a face recognition method based on weighted Huber constrained sparse coding. Background technique [0002] In recent years, face recognition is still a hot research topic. On the one hand, it lies in its huge application potential, on the other hand, it reveals how machine learning can perform feature selection and classification on complete images. Face recognition is considered to be one of the most difficult research topics in the field of biometrics and even in the field of artificial intelligence. On the one hand, this difficulty comes from the characteristics of human biological characteristics: first, the similarity of human faces. The structure and appearance of faces are similar between different individuals. This similarity is not conducive to the use of human faces to distinguish human individuals; secondly, the variability of human faces. The shape of the...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Patents(China)
IPC IPC(8): G06V40/16G06V10/77G06K9/62
CPCG06V40/172G06F18/2136
Inventor 雷大江蒋志杰陈浩张莉萍吴渝
Owner CHONGQING UNIV OF POSTS & TELECOMM
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap