Joint training method of sub-dictionaries in multiple characteristic spaces and for face recognition

A feature space and face recognition technology, applied in the field of image recognition, can solve problems such as poor generalization ability and inability to make full use of features

Inactive Publication Date: 2014-05-14
TIANJIN UNIV
View PDF4 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The two types of dictionary learning methods mentioned above are all learning the original training pictures in a single feature

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
  • Joint training method of sub-dictionaries in multiple characteristic spaces and for face recognition
  • Joint training method of sub-dictionaries in multiple characteristic spaces and for face recognition
  • Joint training method of sub-dictionaries in multiple characteristic spaces and for face recognition

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0031] In order to make the purpose of the present invention, implementation scheme and advantage clearer, the specific implementation of the present invention is described in further detail below, and the concrete process of the present invention is as follows figure 1 shown.

[0032] (1) The original training sample {X 1 …X N} projected to the Eigenface feature space to form a sub-dictionary O E , the sample vector expression after projection in the feature space is Y K =W PCA T x K ,X K is a training sample vector, W PCA It is the matrix composed of the bases of the Eigenface feature space, and the set {Y 1 ...Y K ...Y N} is the sub-dictionary O E .

[0033] (2) The original training sample {X 1 …X N} projected to the Laplacianface feature space to form a sub-dictionary O E , the sample vector expression after projection in the feature space is Y K =W T x K ,W=W PCA W LPP , W PCA Indicates that principal vector analysis is first performed on the original ...

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 invention provides a joint training method of sub-dictionaries in multiple characteristic spaces and for face recognition. The joint training method of the sub-dictionaries in the multiple characteristic spaces and for face recognition comprises the steps that original training samples{X[1] to X[N]} are projected in the Eigenface characteristic space, the Laplacianface characteristic space and the Gabor characteristic space to form the sub-dictionary OE, the sub-dictionary OL and the sub-dictionary OG respectively, and joint optimization training is carried on the three sub-dictionaries by using a genetic algorithm to obtain the sub-dictionary NE, the sub-dictionary NL and the sub-dictionary NG. According to the joint training method of the sub-dictionaries in the multiple characteristic spaces and for face recognition, a sample, with the highest distinguishing capacity, in each sub-dictionary can be selected, and thus accuracy of face recognition on the basis of a sparse classifier using the sub-dictionaries can be improved.

Description

technical field [0001] The invention belongs to the technical field of image recognition and relates to a multi-dictionary joint training method. Background technique [0002] Face recognition has always been a popular subject in the field of computer vision. Wright et al. proposed to use the sparse classifier (SRC) based on compressed sensing theory to recognize faces, and achieved good results. However, the algorithm directly uses the training picture as a sparse representation of the L1 norm constraint on the detection picture, which obviously cannot fully represent the characteristics of the face picture to be tested, and the high number of atoms in the dictionary increases the complexity of the encoding. [0003] Therefore, how to learn the optimal dictionary from the original training samples has become a hot research direction. There are currently many dictionary learning algorithms for face recognition: [0004] 1. Metaface, KSVD, etc. all learn the original traini...

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
IPC IPC(8): G06K9/00G06K9/66
Inventor 金志刚徐楚
Owner TIANJIN UNIV
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