基于大余量度量学习的人脸检测诱导门禁系统

Li'e Pu, Jialin Sun
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引用次数: 0

摘要

随着科学技术的发展和经济一体化进程的加快,身份认证已经成为网络空间最基本的要素,是整个信息安全体系的基础。生物特征识别技术是身份认证过程中的一项重要技术。其中,人脸识别技术以其易于使用、不敏感等固有优势,在身份认证领域受到研究者、社会应用和用户的青睐。本文利用大裕度度量学习技术,建立了一种基于人脸识别的门禁系统。首先,将人脸库输入到深度神经网络中提取表征特征。其次,利用深度表征特征学习大余量度量学习模型;第三,利用数码相机采集人脸图像,输入大余量度量学习模型进行人脸识别。实验结果表明,该系统能够准确地识别出大多数人。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Face Detection-Induced Access Control System via Large Margin Metric Learning
With the development of science and technology and the acceleration of economic integration, identity authentication has become the most basic element in cyberspace and the basis of the whole information security system. Biometric recognition technology is an important technology in the process of identity authentication. Among them, face recognition technology has been favored by researchers, social applications, and users in the field of identity authentication by virtue of its inherent advantages such as ease of use and insensitivity. In this paper, a face recognition-based access control system is established with the help of large margin metric learning. First, a face library is input into a deep neural network to extract representation features. Second, the deep representation features are used to learn a large margin metric learning model. Third, the face image is captured by a digital camera to input into large margin metric learning model for identifying the person. The experimental results show that the proposed system can accurately identify most of the persons.
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