标题: 基于局部二值化模式延伸法之低成本人脸辨识系统
Low-Cost Face Recognition System Based on Extended Local Binary Pattern
作者: 陈琪惠
陈永平
Chen, Qi-Hui
Chen, Yon-Ping
电控工程研究所
关键字: 脸部辨识;局部二值化模式;主成分分析;稀疏表示分类器;Face recognition;local binary pattern;principal component analysis;classification based on sparse representation
公开日期: 2016
摘要: 近年来,以生物识别技术为基础的身分认证系统和物联网应用蓬勃发展,本论文旨在开发一基于局部二值化模式延伸法的人脸辨识系统,并将其应用为门禁系统实行于小尺寸、低成本和低功耗的物联网装置上。所开发之系统包含人脸侦测、特征撷取、人脸辨识三个部份,首先利用Viola-Jones人脸侦测法找出人脸的资讯,以局部二值化模式延伸法撷取人脸特征,接着利用主程分分析将提取后的特征资料投影至低维度空间,最后通过二阶范数稀疏表示之分类器作人脸辨识和认证。本论文所提出之人脸辨识方法,于各人脸资料库实验结果良好,其辨识率皆可达到95%以上,甚至可于Cohn-Kanade人脸资料库达到99%之辨识率。本论文所提之系统作为门禁系统应用于Raspberry PI 3上,可于1秒内完成人脸认证,为一即时人脸辨识系统。
In recent years, the IoT application and the biometric-based authorization become popular. This thesis proposes a face recognition system with high accuracy rate based on extended Local Binary Pattern, and applies it as an access control system on an IoT device which is always low-cost, low-power and small-footprint. The proposed face recognition system includes three parts, face detection, feature extraction and face recognition. For the face detection, the Viola-Jones face detector is adopted to find out the face information. The extended Local Binary Pattern then extracts the local features of the face. Further transform these features to a low-dimension subspace by Principle Component Analysis method. Finally, use the classification based on the sparse representation of L2 norm minimization to identify and verify the face. From the experimental results, the proposed method can achieve a high recognition rate better than 95% in several face databases, even reach 99% for the Cohn-Kanade face database. The access control system implemented on Raspberry Pi 3 is able to complete the whole face recognition in a second, which makes it indeed a real-time system.
URI: http://etd.lib.nctu.edu.tw/cdrfb3/record/nctu/#GT070360011
http://hdl.handle.net/11536/143350
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