Hadamard matrices and their applications
Hadamard matrices and their applications
Inside risks: the uses and abuses of biometrics
Communications of the ACM
CCS '99 Proceedings of the 6th ACM conference on Computer and communications security
High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Enhancing security and privacy in biometrics-based authentication systems
IBM Systems Journal - End-to-end security
Iris Biometric Cryptography for Identity Document
SOCPAR '09 Proceedings of the 2009 International Conference of Soft Computing and Pattern Recognition
Biometrics: a tool for information security
IEEE Transactions on Information Forensics and Security
IEEE Transactions on Circuits and Systems for Video Technology
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As the password based authentication systems are not able to meet the performance because they can be stolen, forgotten, cracked, sniffed and tampered with. Lateral thinking to this problem evolved the use of biometrics to authenticate the person uniquely. Since, the templates are stored in a centralized database there is possibility of tampering of templates. The objective of this paper is to combine cryptography with biometrics by apply minor changes to the iris templates to transform them and store in a database, and hence forth even the system is compromised, the template is safe from the wrong hands thus to improve the security of the system in a network. For determining the performance of the system digitized grayscale eye images from CASIA 1.0[3] iris image set is used. This authentication system consists of an automatic recognition of iris template using the password provided by the user. The system performance is tested using different key sizes 128 bit, 256 bit and 512 bit keys. We used Hamming distance for classification of iris templates, and templates are accepted to match if the statistical independence was failed. Experimental results showed that the system performed with perfect recognition on a set of 756 images and resulted in an accuracy rate of 96%.