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In vivo face detection method based on convolutional neural network

A convolutional neural network, face detection technology, applied in the field of machine learning and pattern recognition

Inactive Publication Date: 2016-09-21
BEIJING UNIV OF TECH
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Problems solved by technology

The diversification of face spoofing methods has brought great challenges to the research of face spoofing detection technology

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  • In vivo face detection method based on convolutional neural network
  • In vivo face detection method based on convolutional neural network
  • In vivo face detection method based on convolutional neural network

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Embodiment Construction

[0025] The technical solutions of the present invention will be described in more detail below in conjunction with the accompanying drawings and examples of implementation.

[0026] The overall framework of the present invention is attached as description figure 1 As shown, it is divided into three parts: face detection, feature extraction and classification.

[0027] A. Sampling the video captured by the camera by frame. The video has a total of 270 frames, and a sample image is taken every 10 frames;

[0028] B. Preprocessing the sample image, including two processing methods: 1. Perform face detection on the sample image. The detection method used is based on the face detection algorithm of adaboost. After the face is detected, the face area is cut out , and normalize all face images to 32*32 pixels; 2. Without face detection, directly normalize the entire image (including face area and background area) to 32*32 pixels.

[0029] C. Use the convolutional neural network to ...

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Abstract

An in vivo face detection method based on a convolutional neural network relates to the machine learning and mode identification field, and is provided to face a cheat problem in the face identification. A conventional face identification technology is very easy to attack, the attackers frequently copy the faces of the legal users by the methods, such as the photographs, the videos, the 3D models, etc., if a face identification system can not distinguish the real faces and the fake faces effectively, the invaders can pass the face identification system very easily by the fake identities. Based on the problem, the present invention provides the in vivo face detection method based on the convolutional neural network. The convolutional neural network in the method provided by the present invention is realized based on a cuda_convnet framework, the network structure comprises four convolutional layers of two max-pooling layers, one full connection layer and one soft_max layer, and the soft_max layer comprises two nerve cells used for predicting the probability distribution of the real and fake faces.

Description

technical field [0001] The invention relates to the fields of machine learning and pattern recognition, in particular to the research and realization of a living human face detection based on a convolutional neural network. Background technique [0002] Face recognition is a popular research field of biometric technology. Compared with other biometric technologies, face recognition technology has the advantages of non-contact and friendliness. Face recognition systems have been used in more and more occasions, such as mobile terminal unlocking systems, computer boot login systems, and access control systems. In addition, face recognition is also used in criminal investigation, surveillance systems and other fields. However, behind the rapid development of face recognition technology, there are huge security risks. The face recognition system can determine the true identity of the face, but it cannot determine whether the face image in front of the camera is from a legitima...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06N3/08
CPCG06N3/08G06V40/161G06V40/40G06V40/45
Inventor 毋立芳许晓漆薇贺娇瑜徐姚文张洪嘉
Owner BEIJING UNIV OF TECH
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