Hidden Markov model based face geometrical feature identification method

A hidden Markov and geometric feature technology, applied in the field of face recognition, can solve the problems of low recognition rate, low recognition accuracy, high interdependence of matching parameters, etc., to achieve the effect of ensuring accuracy and scientificity

Inactive Publication Date: 2015-12-16
镇江锐捷信息科技有限公司
View PDF3 Cites 13 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In view of this, the present invention provides a face geometric feature recognition method based on a hidden Markov model to solve the problem that the current face recognition method has a low recognition rate and the matching parameters used have a high degree of interdependence, which leads to accurate recognition. less technical issues

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
  • Hidden Markov model based face geometrical feature identification method
  • Hidden Markov model based face geometrical feature identification method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0019] The following description and drawings illustrate specific embodiments of the invention sufficiently to enable those skilled in the art to practice them. Other embodiments may incorporate structural, logical, electrical, process, and other changes. The examples merely represent possible variations. Individual components and functions are optional unless explicitly required, and the order of operations may vary. Portions and features of some embodiments may be included in or substituted for those of other embodiments.

[0020] In some illustrative examples, such as figure 1 with 2 As shown, a hidden Markov model-based face geometric feature recognition method is provided, including:

[0021] 101: Establish a database. The database is used to store the acquired data.

[0022] In some demonstrative embodiments, the process of establishing the database includes:

[0023] S1: Obtain multiple frontal images to be stored in the database;

[0024] S2: Process each front...

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 hidden Markov model based face geometrical feature identification method. The method comprises: performing graying and histogram equalization on a face image and obtaining a face frame region of a face by utilizing a classifier; setting a shrinkage coefficient of the face frame region, shrinking the face frame region, and intercepting an eye region, a nose region and a mouth region in the face frame region by utilizing the classifier; extracting and recording feature information; and comparing the obtained feature information with feature information stored in a database, to obtain the comprehensive matching rate. Comparative matching is performed by utilizing length and angle ratios of parts of the face, so that the influence caused by non-rigid and illumination conditions of the face is greatly reduced, the influence caused by change of the length and angle ratios of the parts of the face along with change of age and weight of people is avoided, and the accuracy and scientificity of matching are ensured.

Description

technical field [0001] The invention belongs to the technical field of face recognition, in particular to a face geometric feature recognition method based on a hidden Markov model. Background technique [0002] In today's society, all parties are eager to perform identity verification quickly and effectively. Because of its own stability and differences, biometric features have become the main means of identity verification. There are many related studies. Among them, face recognition is a relatively mature technology. into people's daily life. Compared with the use of retinal recognition, fingerprint recognition and other human biometrics for identity verification, face recognition technology is intuitive, friendly and convenient. It is getting more and more attention and favor, and has a wide range of application prospects. [0003] Human facial features are very rich. In addition to shape and expression, there are also feature distributions of facial features. By studyi...

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 Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/16G06V40/168G06F18/24
Inventor 高志军伍爵博陈婷刘鑫
Owner 镇江锐捷信息科技有限公司
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