Face attribute recognition method and apparatus based on combined depth neural network

An attribute recognition and deep neural network technology, applied in the field of artificial intelligence image recognition, can solve the problems of difficult to obtain ideal results, poor universality, relying on experience, etc., to achieve good scalability, high accuracy, good robustness. awesome effect

Inactive Publication Date: 2018-09-28
INFORMATION SCI RES INST OF CETC
View PDF9 Cites 38 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The first type of method relies too much on experience, is not suitable for multi-source big data processing, and has poor universality. After the emergence of deep learning technology, it has been rarely

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
  • Face attribute recognition method and apparatus based on combined depth neural network
  • Face attribute recognition method and apparatus based on combined depth neural network
  • Face attribute recognition method and apparatus based on combined depth neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0025] The specific implementation of the present invention will be described below in conjunction with the accompanying drawings.

[0026] An embodiment of the present invention provides a face attribute recognition method based on a combined deep neural network, which is used to detect a user's age, gender, expression and wear. The present invention divides age into juvenile (0-10 years old), juvenile (10-18 years old), youth (18-35 years old), middle-aged (35-60 years old) and old age (over 60 years old) five according to facial features. In the first stage, the expression is divided into three types: non-smiling, smiling and laughing, and whether to wear glasses and the type of glasses (ordinary glasses and sunglasses) are identified.

[0027] figure 1 It is a schematic flow chart of face attribute recognition in an embodiment of the present invention, specifically comprising the following steps:

[0028] S1: Extract the region of the input face image;

[0029] S2: Acco...

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 face attribute recognition method based on a combined depth neural network. The method comprises the following steps: S1, performing area extraction on an inputted face image;S2, carrying out positioning on a facial important part and extracting a related area based on the area extraction result to obtain facial area features and facial key part features; S3, marking a real age and a gender of each face by using the area extraction result as a training sample, carrying out training based on a deep neural network, and outputting an age estimation value and a gender recognition result; and S4, on the basis of the facial area features outputted by the S2 and local area features of eyes and mouths, outputting expression and wearing prediction probabilities respectively according to a random forest algorithm, adding probabilities of corresponding attributes for different attributes, and outputting facial expression and wearing attribute results. In addition, the invention also provides a face attribute recognition apparatus based on a combined depth neural network.

Description

technical field [0001] The invention belongs to the technical field of image recognition of artificial intelligence, and relates to a face attribute recognition method and device based on a combined deep network. Background technique [0002] With the development of society and the advancement of science and technology, face attribute recognition has become more and more widely used in many enterprises and institutions such as finance, military, medical care, and public security. Biological characteristics are specific intrinsic attributes of human beings. Due to their complex structure, many changes in details, and certain individual differences and stability, they can be used as a reliable basis for identity verification. The face is a very important biological feature, which can represent human emotions, gender, age and other attributes. Compared with other human biological features, the acquisition of the features of the face is more direct and imperceptible. The most n...

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): G06K9/00G06N3/04
CPCG06V40/165G06V40/174G06V40/171G06V40/193G06N3/045
Inventor 熊荔张峰张德
Owner INFORMATION SCI RES INST OF CETC
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