Abstract. Biometrics-based user authentication has several advantages over traditional password-based systems for standalone authentication applications such as home networks. This is also true for new authentication architectures known as crypto-biometric systems, where cryptography and biometrics are merged to achieve high security and user convenience at the same time. Recently, a cryptographic construct, called fuzzy vault, has been proposed for crypto-biometric systems. In this paper, we propose an approach to provide both the automatic alignment of fingerprint data and higher security by using a 3D geometric hash table. Based on the experimental results, we confirm that the proposed approach of using the 3D geometric hash table with the idea of the fuzzy vault can perform the fingerprint verification securely even with one thousand chaff data included.

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Abstract:The fuzzy vault scheme has emerged as a promising solution to the user privacy and the fingerprint template security problems. Recently, however, this scheme is shown to be susceptible to a correlation attack that finds the real minutiae using multiple vaults enrolled for different applications. To protect the fuzzy fingerprint vault from the correlation attack, we propose an approach to add chaffs in a more structured way such that distinguishing the fingerprint minutiae and the chaff points obtained from two applications is computationally hard. Experimental results show that the proposed approach achieves much secure performance than adding chaffs randomly without a significant degradation of the verification accuracy.

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Biometrics verification can be efficiently used for intrusion detection and intruder identification in video surveillance systems. Biometrics techniques can be largely divided into traditional and the so-called soft biometrics. Whereas traditional biometrics deals with physical characteristics such as face features, eye iris, and fingerprints, soft biometrics is concerned with such information as gender, national origin, and height. Traditional biometrics is versatile and highly accurate. But it is very difficult to get traditional biometric data from a distance and without personal cooperation. Soft biometrics, although featuring less accuracy, can be used much more freely though. Recently, many researchers have been made on human identification using soft biometrics data collected from a distance. In this paper, we use both traditional and soft biometrics for human identification and propose a framework for solving such problems as lighting, occlusion, and shadowing.