Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

Facial feature recognition method and system based on multi-region feature and metric learning

A technology of metric learning and face features, applied in neural learning methods, character and pattern recognition, instruments, etc., can solve the problems of low face recognition accuracy, slow recognition speed, and high feature dimension

Active Publication Date: 2020-11-24
苏州飞搜科技有限公司
View PDF3 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] In the existing face recognition technology, one implementation method is to extract face features + Euclidean distance recognition through single-scale / area training of the face area, but the disadvantage is that the expression ability of the extracted features is limited, and the accuracy of face recognition Low
Another way is to extract face features through face multi-region training + principal component analysis (PCA) dimensionality reduction + joint Bayesian (Joint-Bayesian) method, but its disadvantage is that the recognition speed is slow
Another way is to extract face features from multiple areas of the face + Euclidean distance or cosine distance recognition, but its disadvantage is that the feature dimension is high and the storage space is large.

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
  • Facial feature recognition method and system based on multi-region feature and metric learning
  • Facial feature recognition method and system based on multi-region feature and metric learning
  • Facial feature recognition method and system based on multi-region feature and metric learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0076] The principles of the disclosure will now be described with reference to some example embodiments. It can be understood that these embodiments are described only for the purpose of illustrating and helping those skilled in the art to understand and implement the present disclosure, rather than suggesting any limitation to the scope of the present disclosure. The disclosure described herein may be implemented in various ways other than those described below.

[0077] As used herein, the term "comprising" and its variations may be understood as open-ended terms meaning "including but not limited to". The term "based on" may be understood as "based at least in part on". The term "one embodiment" can be read as "at least one embodiment". The term "another embodiment" may be understood as "at least one other embodiment".

[0078] It can be understood that the following concepts are defined in this embodiment:

[0079] The convolutional neural network is a deep learning a...

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 face feature recognition method and system based on multi-region feature and metric learning. The method includes: obtaining convolutional neural network parameters of corresponding positions and scales through multi-scale face region training, and according to the convolutional neural network The parameters extract the features of the corresponding area of ​​the face; filter the above features to obtain high-dimensional face features; perform metric learning according to the high-dimensional face features, perform dimensionality reduction processing on the features to obtain feature expressions, and then define a loss function. The loss function training is used to obtain a network model of metric learning; after the image to be recognized is input into the network model, the face features are reduced in dimension and then recognized by Euclidean distance. In the present invention, multi-scale selection of multi-regions is used to train the convolutional neural network, which improves the expressive ability of features. At the same time, by selecting the acquired multi-scale features, the expression efficiency of the features is improved, and the accuracy of face recognition is effectively improved.

Description

technical field [0001] The invention relates to the field of image recognition and processing, in particular to a face feature recognition method and system based on multi-region feature and metric learning. Background technique [0002] Face recognition technology is based on human facial features. For the input face image or video stream, it first judges whether there is a face, and if there is a face, it further gives the position, size and each face of each face. Location information of major facial organs. Based on this information, the identity features contained in each face are further extracted, and compared with known faces, so as to identify the identity of each face. Face recognition technology consists of three parts: 1) face detection, 2) face tracking, and 3) face comparison. [0003] In the existing face recognition technology, one implementation method is to extract face features + Euclidean distance recognition through single-scale / area training of the fa...

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): G06K9/00G06N3/08
CPCG06N3/08G06V40/161G06V40/168
Inventor 郭宇白洪亮董远
Owner 苏州飞搜科技有限公司
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
Eureka Blog
Learn More
PatSnap group products