A face living body recognition method based on multi-classifier fusion of deep learning

A multi-classifier fusion, deep learning technology, applied in biometric recognition, character and pattern recognition, instruments, etc., can solve the problem of inability to resist video attacks, and achieve the effect of robust recognition

Pending Publication Date: 2019-04-23
ZHEJIANG UNIV
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AI Technical Summary

Problems solved by technology

In general, the current face recognition algorithm research focuses on feature extraction and classifier design. Most

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  • A face living body recognition method based on multi-classifier fusion of deep learning
  • A face living body recognition method based on multi-classifier fusion of deep learning
  • A face living body recognition method based on multi-classifier fusion of deep learning

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Embodiment Construction

[0028] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments.

[0029] The present invention provides a human face recognition method based on deep learning and multi-classifier fusion. The method is to use traditional edge feature frame detection, collect data and train deep neural network for moiré detection, and use human eye key Eyeblink detection at the point position, and live face classification and recognition based on multi-classifier fusion.

[0030] The detailed steps are as follows:

[0031] 1. Algorithm process of living body recognition based on multi-classifier fusion, such as figure 1 :

[0032] (a) Blink detection: For the video frame f to be detected in the video F i Perform face key point detection, obtain the key point coordinates of the eye area, and calculate the human eye opening and closing degree U from the key point coordinates of the eye area i , the statistics of hu...

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Abstract

The invention discloses a human face living body recognition method based on multi-classifier fusion of deep learning, which comprises the following steps of respectively training three classifiers byusing traditional edge features, eye features and the like and deep neural network features, and then carrying out the human face living body recognition by using the classifiers through a designed program process. The method aims at a living body identification problem in a real scene. Frame detection, blink detection and moire detection are designed, the three detection results are fused innovatively according to a certain logic to obtain a human face living body recognition result, and the method has the advantages of being good in anti-cheating capacity, excellent in anti-interference capacity, capable of being matched by a small number of users, only needing a common camera (without additional equipment) and the like. The method mainly comprises the steps of the border detection, theblink detection, the moire detection, the fusion judgment and the like, and makes a certain contribution to the application of practical popularization of human face living body recognition.

Description

technical field [0001] The invention relates to scientific research fields such as machine learning, deep learning and pattern recognition, and in particular to a human face recognition method based on deep learning and multi-classifier fusion. Background technique [0002] Face liveness detection has become a key component of face recognition. It is a very important and necessary link for any biological detection system. It can ensure that the biological detection system can work safely and effectively; for unsupervised face For the application of recognition systems, automatically resisting photo and video spoofing is an urgent problem in the field of face recognition. [0003] The detection of live faces mainly includes the following methods: three-dimensional depth information analysis, optical flow estimation of facial motion, mixed face and voice recognition, Fourier spectrum analysis, eye blink detection, thermal infrared imaging recognition, etc. And the fusion of s...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/161G06V40/172G06V40/168G06V40/18G06V40/45G06F18/254
Inventor 毛颖胡浩基王曰海
Owner ZHEJIANG UNIV
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