基于深度学习的人脸识别方法研究

doi:10.16180/j.cnki.issn1007-7820.2019.06.017

摘要/Abstract

摘要：

作为非接触式生物识别方法之一,人脸识别在诸多情况下被广泛使用。然而,传统的人脸识别方法由于识别准确度低以及在多个场合的应用受到限制,已不能满足目前的需求。文中提出了采用深度学习的方法来实现脸部标志检测和无限制人脸识别。为解决人脸标志检测问题,使用一种深层卷积神经网络的逐层训练方法,以帮助卷积神经网络进行收敛,并提出了一种避免过拟合的样本变换方法;为了解决人脸识别问题,文中提出了一种SIAMESE卷积神经网络,其在不同部位和尺度上进行训练。实验测试显示,ORL和人脸识别算法的精度分别达到了91%和81%。

关键词: 脸部标志检测, 无限制人脸识别, 深度学习, 卷积神经网络, SIAMESE网络

Abstract:

As one of the contactless biometric methods, face recognition is widely used in many situations. However, the traditional face recognition method can not meet the current demand because of its low recognition accuracy and limited application in many occasions. In this paper, we proposed a deep learning method to achieve facial marker detection and unrestricted face recognition. In order to solved the problem of face marker detection, this paper proposed a layer-by-layer training method for deep convolutional neural networks to help convolutional neural network convergence, and proposes a sample transformation method to avoided overfitting;In order to solve the face recognition problem, this paper proposed a SIAMESE convolutional neural network, which was trained on different parts and scales. Experimental tests showed that the accuracy of ORL and face recognition algorithms reached 91% and 81%, respectively.

Key words: facial Landmark detection, unrestricted face recognition, deep learning, convolutional neural network, SIAMESE network

中图分类号:

TP391.4

胡少聪. 基于深度学习的人脸识别方法研究[J]. 电子科技, 2019, 32(6): 82-86.

HU Shaocong. Research on Face Recognition Based on Deep Learning[J]. Electronic Science and Technology, 2019, 32(6): 82-86.

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