Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Multi-task deep feature space attitude face recognition method

A deep feature and face recognition technology, which is applied in the fields of computer vision and artificial intelligence, can solve the problems of difficult face recognition rate and affecting face recognition rate, etc.

Active Publication Date: 2019-09-24
SOUTHEAST UNIV
View PDF4 Cites 9 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, it is difficult to improve the recognition rate of pose faces through pose correction at the image level, because the corrected front faces are easily affected by factors such as expressions, lighting, and occlusion caused by poses, which in turn affects face recognition. Rate

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
  • Multi-task deep feature space attitude face recognition method
  • Multi-task deep feature space attitude face recognition method
  • Multi-task deep feature space attitude face recognition method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0061] The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments.

[0062] like figure 1 As shown, the present invention provides a multi-task-based deep feature space face posture correction recognition method, which specifically includes the following steps:

[0063] 1) The first is the preparation of the dataset. For the training set, the preparation of the face image, the preprocessing of the face image and the determination of the posture deflection angle of the face image are mainly carried out. The specific steps are as follows:

[0064] 1.1) Preparation of face image. During the training process, the dataset uses the MS-Celeb-1M face database. The face database is provided by Microsoft Research Asia, which searches 100,000 celebrities from the Internet, and each person has about 100 pictures (about 10 million faces in total). However, the original database has a lot of noise, so it is necessary to ...

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 multi-task depth feature space attitude face recognition method, which comprises the following steps: firstly, carrying out angle measurement on an attitude face image, and extracting image depth space features by utilizing a residual network; then, adding a residual transformation mapping module to realize transformation from the side face depth feature to the front face depth feature, so that a main task of the network is formed; then, adding a module on the basis of an original residual transformation mapping module to realize reconstruction of original side face depth characteristics so as to realize feedback which is a secondary task of the network; and finally, using the cosine similarity to measure the similarity between the to-be-compared face and the depth feature representation of all people in the database, so that face authentication recognition is carried out. According to the method, the robust representation of the front face depth space feature can be obtained according to the side face depth space feature, so that the side face recognition rate is greatly improved, and the method has a very good application prospect in attitude face detection and recognition.

Description

technical field [0001] The invention belongs to the technical field of computer vision and artificial intelligence, and relates to a face recognition method, in particular to a multi-task deep feature space gesture face recognition method. Background technique [0002] Face recognition technology is one of the hot research topics in the fields of contemporary artificial intelligence, pattern recognition, and computer vision. Face recognition technology is used in a wide range of fields, such as public security, e-commerce and information security. In the face recognition detection in practical applications, it is concluded that the angle and posture factors are the main factors affecting the face recognition results. When the input face image is a side face image with a large deflection angle, the performance of many conventional face recognition algorithms will drop significantly, resulting in a significant drop in recognition rate. Therefore, it is of great value and sig...

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): G06K9/00G06K9/62G06N3/04
CPCG06V40/161G06V40/168G06N3/045G06F18/214Y02D10/00
Inventor 王辰星程超达飞鹏
Owner SOUTHEAST UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com