Monogenic multi-characteristic face expression identification method based on sparse fusion

A facial expression recognition and facial expression technology, applied in the field of pattern recognition, can solve problems such as failure to achieve recognition effect

Active Publication Date: 2016-08-31
HEFEI UNIV OF TECH
View PDF6 Cites 17 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in complex situations, a single featur

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
  • Monogenic multi-characteristic face expression identification method based on sparse fusion
  • Monogenic multi-characteristic face expression identification method based on sparse fusion
  • Monogenic multi-characteristic face expression identification method based on sparse fusion

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0081] In this example, if figure 1 As shown, a facial expression recognition method based on the sparse fusion of single-shot and multi-features can fully extract the texture, shape and direction features of the facial expression image. By performing single-shot filtering on the preprocessed expression image, three single-scale amplitude information, single-scale direction information, single-scale phase information, horizontal transformation information, and vertical transformation information on each scale; and then use five kinds of single-scale information to extract the histogram features of the single-value binary mode from the facial expression image, The direction histogram feature and the phase histogram feature are derived alone, and the corresponding sparse dictionaries are respectively constructed for the three features; finally, the weights of the three sparse dictionaries are optimized using the regularized least square method, and facial expression recognition i...

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 discloses a monogenic multi-characteristic face expression identification method based on sparse fusion. The method comprises the following steps of: 1, carrying out monogenic filtering on an expression image after pre-processing, and obtaining monogenic amplitude information, monogenic direction information, monogenic phase information, transverse conversion information and longitudinal conversion information in three dimensions; 2, utilizing five kinds of monogenic information to extract a monogenic binary mode histogram characteristic, a monogenic direction histogram characteristic and a monogenic phase histogram characteristic of the face expression image, wherein the three kinds of characteristics respectively construct corresponding sparse dictionaries; and 3, utilizing a l1 regularization least square method to optimize weights of the three sparse dictionaries, and realizing face expression identification by means of weighted fusion. According to the invention, the texture, shape and direction characteristics of the face expression image can be fully extracted, and the expression identification rate is improved.

Description

technical field [0001] The invention relates to a feature extraction method and classification discrimination, and belongs to the field of pattern recognition, in particular to a single-play multi-feature human facial expression recognition method based on sparse fusion. Background technique [0002] As an interdisciplinary subject of psychology and computer science, facial expression recognition is an important part of the field of artificial intelligence and human-computer interaction. Usually the facial expression recognition process is divided into three steps: image preprocessing, feature extraction, and expression recognition. [0003] Feature extraction is an important process of facial expression recognition. Typical facial expression feature extraction methods include: local binary pattern (Local binary pattern, LBP), Gabor wavelet and improved algorithms based on the two, such as the fusion of LBP method and Gabor The LGBP feature of the wavelet method can enhance...

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): G06K9/00
CPCG06V40/174G06V40/172
Inventor 郑瑶娜胡敏余子玺滕文娣张柯柯王晓华任福继孙晓
Owner HEFEI UNIV OF TECH
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