Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Large-scale face identification method based on GPU accelerated retrieval

A face recognition, large-scale technology, applied in the field of large-scale face recognition based on GPU-accelerated retrieval, can solve the problem of rarely designing hash indexes, to ensure recognition accuracy, reduce retrieval time consumption, and high recognition accuracy rate effect

Active Publication Date: 2018-01-12
武汉世纪金桥安全技术有限公司 +1
View PDF3 Cites 29 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in the research work of approximate search based on semantic hashing method, most of them are researching hash generation method, and there are few studies on designing hash index to improve retrieval efficiency.

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
  • Large-scale face identification method based on GPU accelerated retrieval
  • Large-scale face identification method based on GPU accelerated retrieval
  • Large-scale face identification method based on GPU accelerated retrieval

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0051] The present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments.

[0052] The terms used in the embodiments of the present invention are explained as follows:

[0053] MTCNN: Multi-task convolutional neural network, multi-task convolutional neural network;

[0054] CNN: convolutional neural network, convolutional neural network;

[0055] PReLU (Parametric Rectified Linear Unit): Activation function with parameters.

[0056] see figure 1 As shown, the embodiment of the present invention provides a kind of large-scale face recognition method based on GPU accelerated retrieval, comprising the following steps:

[0057] S1, input the picture to be detected into the MTCNN network, adopt the face detection algorithm, detect the position and key point position of the face in the photo, and align the detected faces;

[0058] S2, use the trained deep learning model to extract step S1 and process the face photo and ...

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 large-scale face identification method based on GPU accelerated retrieval, which relates to the field of computer vision. The large-scale face identification method comprisesthe steps of face detection and alignment, face feature extraction, Hash feature acquisition, face index database establishment, multi-GPU accelerated Rough matching, hash-based candidate set acquisition, precise matching based on distance metric, voting to obtain a best-matched person and the like. The large-scale face identification method based on GPU accelerated retrieval is based on two-stage feature matching of Hash index and multi-GPU accelerated computing, can accelerate the screening of eigenvectors by utilizing the powerful parallel computing capacity of the GPU, greatly reduces time consumption on large-scale data set retrieval, and can meet various application requirements taking the implementation of a deep convolutional neural network as the basis and having high demand on real-time performance.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to a large-scale face recognition method based on GPU accelerated retrieval. Background technique [0002] In recent years, with the rapid improvement of computer performance and the continuous improvement of deep learning methods, major breakthroughs have been made in the fields of pattern recognition and artificial intelligence. People have achieved excellent results in many pattern recognition tasks through deep learning methods, and face recognition is no exception. With the advent of the big data era, face image data is becoming more and more abundant. How to efficiently and accurately identify a person's identity information in a large-scale face data set is a research hotspot in the field of pattern recognition and information retrieval today. [0003] As an important means of identifying identity, face recognition has extremely high theoretical and practical value. Face-base...

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): G06K9/00G06K9/62G06N3/04
Inventor 邹复好曹锋李开王浩白兴强栾朝阳
Owner 武汉世纪金桥安全技术有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com