Vijaya Kumar B V K, Savvides Marios, Xie Chunyan, Venkataramani Krithika, Thornton Jason, Mahalanobis Abhijit
Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA.
Appl Opt. 2004 Jan 10;43(2):391-402. doi: 10.1364/ao.43.000391.
Using biometrics for subject verification can significantly improve security over that of approaches based on passwords and personal identification numbers, both of which people tend to lose or forget. In biometric verification the system tries to match an input biometric (such as a fingerprint, face image, or iris image) to a stored biometric template. Thus correlation filter techniques are attractive candidates for the matching precision needed in biometric verification. In particular, advanced correlation filters, such as synthetic discriminant function filters, can offer very good matching performance in the presence of variability in these biometric images (e.g., facial expressions, illumination changes, etc.). We investigate the performance of advanced correlation filters for face, fingerprint, and iris biometric verification.
使用生物识别技术进行受试者验证，相比基于密码和个人识别码的方法，能显著提高安全性，因为人们往往会丢失或忘记密码和识别码。在生物识别验证中，系统会尝试将输入的生物特征（如指纹、面部图像或虹膜图像）与存储的生物特征模板进行匹配。因此，相关滤波器技术是生物识别验证所需匹配精度的理想选择。特别是先进的相关滤波器，如合成判别函数滤波器，在这些生物特征图像存在变化（如面部表情、光照变化等）的情况下，能提供非常好的匹配性能。我们研究了先进相关滤波器在面部、指纹和虹膜生物识别验证中的性能。
