Changeable Biometrics for Appearance Based Face Recognition
Changeable Biometrics for Appearance Based Face Recognition
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用于基于外观的人脸识别的可变生物识别技术
DOI：
10.1109/bcc.2006.4341629
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发表时间：
2006
期刊：
影响因子：
--
通讯作者：
Jaihie Kim
中科院分区：
文献类型：
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作者：
MinYi Jeong;Chulhan Lee;Jongsun Kim;Jeung;K. Toh;Jaihie Kim
为了增强生物识别中的安全性和隐私性，最近引入了可改变（或可取消）的生物识别。这个想法是将生物特征信号或特征转换为新的信号或特征，以进行注册和匹配。在本文中，我们提出了可变的生物特征的人脸识别使用的外观为基础的方法。从输入的人脸图像中提取的PCA和伊卡系数向量使用其范数进行归一化。对这两个归一化向量进行随机置乱，并且通过将这两个归一化向量相加来生成新的变换后的面部系数向量（变换后的模板）。当变换后的模板受损时，通过使用新的加扰规则来替换它。由于变换后的模板是由两个矢量相加而成的，因此不能从变换后的系数中恢复出原始的PCA和伊卡系数。在我们的实验中，我们比较了当PCA和伊卡系数向量用于验证时和当变换后的系数向量用于验证时的情况下的性能。
To enhance security and privacy in biometrics, changeable (or cancelable) biometrics have recently been introduced. The idea is to transform a biometric signal or feature into a new one for enrollment and matching. In this paper, we proposed changeable biometrics for face recognition using an appearance based approach. PCA and ICA coefficient vectors extracted from an input face image are normalized using their norm. The two normalized vectors are scrambled randomly and a new transformed face coefficient vector (transformed template) is generated by addition of the two normalized vectors. When a transformed template is compromised, it is replaced by using a new scrambling rule. Because the transformed template is generated by the addition of two vectors, the original PCA and ICA coefficients cannot be recovered from the transformed coefficients. In our experiment, we compared the performance between the cases when PCA and ICA coefficient vectors are used for verification and when the transformed coefficient vectors are used for verification.