Three measures for secure palmprint identification
Pattern Recognition
Proceedings of the 2008 ACM workshop on Secure web services
Personal Identification Using Palmprint and Contourlet Transform
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
A survey of palmprint recognition
Pattern Recognition
An Analysis of Gabor Detection
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Palmprint recognition using 3-D information
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A novel user-participating authentication scheme
Journal of Systems and Software
Information Sciences: an International Journal
A feature level multimodal approach for palmprint identification using directional subband energies
Journal of Network and Computer Applications
A Comparative Study of Palmprint Recognition Algorithms
ACM Computing Surveys (CSUR)
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Biometric authentication systems are widely applied because they offer inherent advantages over classical knowledge-based and token-based personal-identification approaches. This has led to the development of products using palmprints as biometric traits and their use in several real applications. However, as biometric systems are vulnerable to replay, database, and brute-force attacks, such potential attacks must be analyzed before biometric systems are massively deployed in security systems. This correspondence proposes a projected multinomial distribution for studying the probability of successfully using brute-force attacks to break into a palmprint system. To validate the proposed model, we have conducted a simulation. Its results demonstrate that the proposed model can accurately estimate the probability. The proposed model indicates that it is computationally infeasible to break into the palmprint system using brute-force attacks