Access control by RFID and face recognition based on neural network
Access control by RFID and face recognition based on neural network
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DOI：
10.1109/icmlc.2010.5580558
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发表时间：
2010-07
期刊：
影响因子：
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通讯作者：
Dong-Liang Wu;Wing W. Y. Ng;P. Chan;Hai-Lan Ding;Bing-Zhong Jing;D. Yeung
中科院分区：
文献类型：
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作者：
Dong-Liang Wu;Wing W. Y. Ng;P. Chan;Hai-Lan Ding;Bing-Zhong Jing;D. Yeung
射频识别（RFID）技术在门禁系统中得到了广泛的应用。然而，持有RFID卡通过访问控制的人可能不是被授权的人。因此，本文提出了一种结合RFID技术和基于神经网络的人脸识别的门禁系统。系统识别持有RFID卡的人的面部，如果不匹配，则拒绝访问。我们采用径向基函数神经网络（RBFNN）学习的授权卡持有人的脸，并保存RBFNN的参数。当持卡人的数量变大时，这可以减少存储。提取主成分分析（PCA）和线性判别分析（LDA）特征来降低人脸图像数据的维数。采用局部泛化误差模型（L-GEM）训练RBF神经网络，以提高其泛化能力。人脸识别系统首先通过对ORL人脸图像数据库的基准测试进行评估。然后在真实的环境中对整个访问控制系统进行了测试。实验结果表明，该方法具有良好的性能，能够提高RFID门禁的安全性。
Radio frequency identification (RFID) technology has been widely adopted in access control system. However, the people holding the RFID card passing through the access control may not be the authorized one. Therefore, an access control system combining RFID technology and face recognition based on neural network is presented in this work. The system recognizes the face of the person holding the RFID card and denies access if they do not match. We adopt a Radial Basis Function Neural Network (RBFNN) to learn the face of authorized card holders and save the parameters of RBFNN only. This could reduce storage when the number of card holders getting large. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) features are extracted to reduce the dimensions of face image data. The Localized Generalization Error Model (L-GEM) is adopted to train a RBFNN for better generalization capability. The face recognition system is first evaluated by benchmarking ORL face image database. The whole access control system is then tested in a real environment. Experimental results show that the proposed method has a good performance and could improve the security of RFID access control.