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Video face recognition method based on attention set metric learning (ASML)

A metric learning and facial recognition technology, applied in the field of video facial recognition based on attention-set metric learning, can solve the problems of low resolution, technical difficulty of video facial recognition, pose change and motion blur, etc., to make good use of information Effect

Inactive Publication Date: 2017-09-15
SHENZHEN WEITESHI TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, due to lighting changes, low resolution, pose changes, and blur caused by motion, etc., it brings certain difficulties to the research of video face recognition technology.

Method used

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  • Video face recognition method based on attention set metric learning (ASML)
  • Video face recognition method based on attention set metric learning (ASML)
  • Video face recognition method based on attention set metric learning (ASML)

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Embodiment Construction

[0045] It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present invention will be further described in detail below in conjunction with the drawings and specific embodiments.

[0046] figure 1 It is a system flow chart of a video face recognition method based on attention-set metric learning in the present invention. It mainly includes metric learning (ASML) of attention set, memorizing attention weights, and naturally integrating ASML into convolutional neural network (CNN).

[0047] Attention-set metric learning (ASML), which corrects for sample bias and measures correlations between groups of face images, narrows the gap between probability distributions for the same set while widening the gap between different sets; and The end-to-end trainable deep convolutional neural network (CNN) combined with ASML can learn more discriminative deep representations a...

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Abstract

The invention provides a video face recognition method based on attention set metric learning (ASML), which mainly comprises steps of ASML, memory attention weight and natural integration of the ASML to a convolutional neural network (CNN). The process is that an effective distance metric on an image set is defined firstly, the distance within a set is minimized significantly, the concentration distance is maximized at the same time, the weight is used as a nerve Turing machine, a face feature set is used as a memory, the weight is used as an address read-write memory, the ASML is finally naturally integrated to the convolutional neural network, and an end-to-end learning scheme is thus formed. The gap between probability distributions of the same set is narrowed, and the gap between different sets is expanded at the same time; sample offset and noise in the video or image set is reduced, the information in the video or image set is used effectively, and the recognition performance is thus improved.

Description

technical field [0001] The invention relates to the field of face recognition, in particular to a method for video face recognition based on attention-set metric learning. Background technique [0002] Due to the rapid popularization of video surveillance, many video surveillance applications urgently need a long-distance and rapid identification technology in the non-cooperative state of the user, in order to quickly confirm the identity of personnel at a long distance and realize intelligent early warning. Face recognition technology can search for faces in real time from surveillance video images and compare them with face databases in real time to achieve rapid identity recognition. Therefore, video face recognition is widely used in assisting public security criminal investigations, access control systems, camera surveillance systems, identification and payment systems, etc. However, due to lighting changes, low resolution, pose changes, and blur caused by motion, etc....

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06N3/08G06K9/40G06N99/00
CPCG06N3/08G06N20/00G06V40/165G06V10/30
Inventor 夏春秋
Owner SHENZHEN WEITESHI TECH
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