Human face attribute prediction method and apparatus based on deep study and multi-task study

A multi-task learning and deep learning technology, applied in the field of face attribute prediction, can solve the problems of weak attribute value expression ability, complicated calculation process, and insufficient results of the face attribute prediction method, so as to achieve improved prediction effect and obvious prediction effect. Effect

Inactive Publication Date: 2016-03-16
SENSETIME GRP LTD
View PDF13 Cites 60 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0009] In order to solve the problems that the results of the face attribute prediction method in the prior art are not good enough, the calculation process is complicated

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
  • Human face attribute prediction method and apparatus based on deep study and multi-task study
  • Human face attribute prediction method and apparatus based on deep study and multi-task study
  • Human face attribute prediction method and apparatus based on deep study and multi-task study

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0027] combined with figure 1 The face attribute prediction method based on deep learning and multi-task learning proposed by the present invention is described in detail.

[0028] as attached figure 1 As shown, the face attribute prediction method based on deep learning and multi-task learning includes the following steps:

[0029] Step S1: Collect face pictures and label the corresponding categories of multiple attributes to form a training data set.

[0030] The category of face attributes consists of local attributes and global attributes. Local attributes include but are not limited to hair color, hair length, eyebrow length, thick or thin eyebrows, eye size, eyes open or closed, nose bridge height, mouth size, mouth open or closed, whether to wear glasses, whether to wear sunglasses , whether to wear a mask, etc. Global attributes include but are not limited to race, gender, age, appearance, expression, etc.

[0031] For the collected face pictures, manually mark th...

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 human face attribute prediction method and apparatus based on deep study and multi-task study. The face attribute prediction method mainly comprises: collecting a human face, marking a category corresponding to a plurality of attributes, and forming a training data set; detecting a human face and key points of the human face, and aligning the human face via a plurality of key points; encoding the sequential attributes in the category; constructing a deep neural network; and using the training data set to train the deep neural network; deploying a neural network model obtained via training; and finally, using the neural network model to predict the human face attribute in a picture. According to the human face attribute prediction method provided by the invention, through united training of a plurality of attributes, a plurality of attributes can be predicted simultaneously with only one deep network, and a prediction result is improved obviously.

Description

technical field [0001] The present invention relates to the field of image processing, in particular to a face attribute prediction method and device based on deep learning and multi-task learning. Background technique [0002] At present, predicting face attributes from face images has attracted more and more attention. Face attributes include expression, action unit, gender, age, race, mouth size, nose bridge height, whether to wear glasses, whether to wear sunglasses, eye size, eyes open or closed, mouth open or closed, hair length or Hair style, face value, front or side view, etc. Face attribute prediction technology is now widely used in human-computer interaction, user modeling and other fields. [0003] The existing face attribute prediction is mainly based on the traditional machine learning framework. First, the artificially designed features are extracted, and then the dimensionality of the features is reduced to obtain compact features. Finally, the classificat...

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
CPCG06V40/172
Inventor 张伟旷章辉
Owner SENSETIME GRP LTD
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