Full convolution network based facial feature positioning and distinguishing method and system

A technology of facial features and discrimination method, which is applied in the field of face recognition of image processing, can solve the problems of low accuracy and precision of facial feature recognition, and cannot meet the application requirements of many details and high precision, and achieves the key to avoidance. The point is not stable enough, avoiding the lack of key points, and the effect of high-precision facial feature recognition

Inactive Publication Date: 2016-02-24
BEIJING SENSETIME TECH DEV CO LTD
View PDF3 Cites 100 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In order to solve the problem in the prior art that the accuracy and precision of face and facial features recognition are not high and cannot meet the application requireme

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
  • Full convolution network based facial feature positioning and distinguishing method and system
  • Full convolution network based facial feature positioning and distinguishing method and system
  • Full convolution network based facial feature positioning and distinguishing method and system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0022] Attached below Figure 1-3 and the specific implementation manner, the face facial features location and discrimination method based on the full convolutional network in this application will be further described in detail.

[0023] See attached figure 1 As shown, the method for positioning and discriminating facial features of a human face comprises the following steps:

[0024] Step 11: Collect face pictures and mark facial features to form a training data set.

[0025] For the collected face pictures, the facial features categories are manually marked. Feature categories include, but are not limited to, face, left eye, right eye, left eyebrow, right eyebrow, nose, upper lip, lower lip, middle of mouth, tongue, and background.

[0026] In order to obtain a better training effect, preferably, the manual labeling is pixel-level labeling. For the collected face images, the facial features are marked according to the pixels, and the border areas are marked according ...

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 full convolution network based facial feature positioning and distinguishing method and system. The method specifically comprises steps as follows: collecting facial images and marking facial features to form a training data set; designing a full convolution neural network; training the full convolution neural network according to the training data set; performing facial feature positioning and marking on the facial images according to the trained full convolution neural network. According to an image segmentation mode adopting the full convolution network, a predication category of each point of an input face can be obtained, so that the accurate facial feature positions can be obtained, and the problems that the number of key points is insufficient and the key points are not stable enough on the basis of the facial key points are solved.

Description

technical field [0001] The invention relates to the field of face recognition in image processing, in particular to a method and system for locating and distinguishing facial features based on a fully convolutional network. Background technique [0002] As an important biological feature of the human body, the face has played an increasingly important role in image processing, visual technology, information security and other fields in recent years. In the face, the discrimination and positioning technology of facial features is the basis of face recognition, face tracking and other applications. The existing face facial features positioning technology is mainly realized by predicting some pre-designed key points, such as the corners of the eyes, eyebrows, and mouth. Generally, the number of common face key points is 21 key points per face. However, this method based on facial key point positioning has a small number of key point positions, and the recognition accuracy and...

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): G06K9/00G06K9/62
CPCG06V40/165G06V40/172G06F18/214
Inventor 石建萍梁继隋凌志
Owner BEIJING SENSETIME TECH DEV CO 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