Convolutional neural network-based face detection method

A convolutional neural network, face detection technology, applied in biological neural network models, neural architectures, instruments, etc., can solve problems such as poor profile detection and difficult detection, achieve good adaptation effects, ensure accuracy, and improve The effect of accuracy and recall

Inactive Publication Date: 2018-05-29
SHANGHAI JIAO TONG UNIV
View PDF5 Cites 13 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] Although the above-mentioned existing methods can obtain certain accuracy, they still have the following disadvantages: 1. Sensitive to face occlusion, and it is difficult to detect when there are many occlusions. The mAP on WIDER FACE is only 0.77; 2. For smaller people Poor face or profile detection

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
  • Convolutional neural network-based face detection method
  • Convolutional neural network-based face detection method
  • Convolutional neural network-based face detection method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0036] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is carried out on the premise of the technical solution of the present invention, and detailed implementation and specific operation process are given, but the protection scope of the present invention is not limited to the following embodiments.

[0037] The present invention provides a kind of face detection method based on convolution neural network, comprises the following steps: 1) establishes face detection model, and this model adopts RFCN network structure, and described RFCN network structure comprises the feature extraction layer based on feature fusion; 2 ) to obtain a sample set; 3) to train the face detection model established in step 1); 4) to perform face detection on the picture to be tested with the trained face detection model. Through the above method, face detection with higher accuracy and recall rate can be...

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 relates to a convolutional neural network-based face detection method. The method comprises the following steps that: 1) a face detection model is established, the model adopts an RFCN network structure, wherein the RFCN network structure comprises a feature fusion-based feature extraction layer; 2) a sample set is obtained; 3) the face detection model established in step 1) is trained; and 4) the trained face detection model is adopted to perform face detection on a picture to be detected. Compared with the prior art, the method of the present invention has the advantages of high accuracy, high recall ratio, good adaptability for complex scenes, and the like.

Description

technical field [0001] The invention relates to the technical field of face recognition, in particular to a face detection method based on a convolutional neural network. Background technique [0002] Face detection is a research topic involving computer vision, pattern recognition, artificial intelligence and other fields. Because of its wide application value in commercial, medical and military fields, it has always been a hot research topic. However, in real-world scenarios, faces in complex images are often severely occluded, which poses a huge challenge to face detection, so it is still a research to propose a face detection method that can adapt to severe occlusions. difficulty. [0003] Literature "Object Detection via Region-based Fully Convolutional Networks" (Dai, J., Li, Y., He, K., Sun, J.: R-FCN:.In: 30th Conference on Neural Information Processing Systems, pp.379 -387.Barcelona) discloses a target detection method based on regional fully convolutional neural ...

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
CPCG06V40/172G06V40/168G06N3/045G06F18/214
Inventor 刘琳姜飞申瑞民
Owner SHANGHAI JIAO TONG UNIV
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