Low-Cost Face Recognition System Based on Extended Local Binary Pattern
碩士 === 國立交通大學 === 電控工程研究所 === 104 === 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...
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ndltd-TW-104NCTU54490592017-09-06T04:22:13Z http://ndltd.ncl.edu.tw/handle/49329530039264587452 Low-Cost Face Recognition System Based on Extended Local Binary Pattern 基於局部二值化模式延伸法之低成本人臉辨識系統 Chen, Qi-Hui 陳琪惠 碩士 國立交通大學 電控工程研究所 104 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. Chen, Yon-Ping 陳永平 2016 學位論文 ; thesis 52 en_US |
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碩士 === 國立交通大學 === 電控工程研究所 === 104 === 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.
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author2 |
Chen, Yon-Ping |
author_facet |
Chen, Yon-Ping Chen, Qi-Hui 陳琪惠 |
author |
Chen, Qi-Hui 陳琪惠 |
spellingShingle |
Chen, Qi-Hui 陳琪惠 Low-Cost Face Recognition System Based on Extended Local Binary Pattern |
author_sort |
Chen, Qi-Hui |
title |
Low-Cost Face Recognition System Based on Extended Local Binary Pattern |
title_short |
Low-Cost Face Recognition System Based on Extended Local Binary Pattern |
title_full |
Low-Cost Face Recognition System Based on Extended Local Binary Pattern |
title_fullStr |
Low-Cost Face Recognition System Based on Extended Local Binary Pattern |
title_full_unstemmed |
Low-Cost Face Recognition System Based on Extended Local Binary Pattern |
title_sort |
low-cost face recognition system based on extended local binary pattern |
publishDate |
2016 |
url |
http://ndltd.ncl.edu.tw/handle/49329530039264587452 |
work_keys_str_mv |
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1718527294184620032 |