Secured Cryptographic Key Generation From Multimodal Biometrics Feature Level Fusion Of Fingerprint And Iris

Human users have a tough time remembering long cryptographic keys. Hence, researchers, for so long, have been examining ways to utilize biometric features of the user instead of a memorable password or passphrase, in an effort to generate strong and repeatable cryptographic keys. Our objective is to incorporate the volatility of the users biometric features into the generated key, so as to make the key unguessable to an attacker lacking significant knowledge of the users biometrics. We go one step further trying to incorporate multiple biometric modalities into cryptographic key generation so as to provide better security. In this article, we propose an efficient approach based on multimodal biometrics (Iris and fingerprint) for generation of secure cryptographic key. The proposed approach is composed of three modules namely, 1) Feature extraction, 2) Multimodal biometric template generation and 3) Cryptographic key generation. Initially, the features, minutiae points and texture properties are extracted from the fingerprint and iris images respectively. Subsequently, the extracted features are fused together at the feature level to construct the multibiometric template. Finally, a 256bit secure cryptographic key is generated from the multibiometric template. For experimentation, we have employed the fingerprint images obtained from publicly available sources and the iris images from CASIA Iris Database. The experimental results demonstrate the effectiveness of the proposed approach.Comment: Pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS January 2010, ISSN 1947 5500, http://sites.google.com/site/ijcsis

Sparsely Grouped Input Variables for Neural Networks

In genomic analysis, biomarker discovery, image recognition, and other systems involving machine learning, input variables can often be organized into different groups by their source or semantic category. Eliminating some groups of variables can expedite the process of data acquisition and avoid over-fitting. Researchers have used the group lasso to ensure group sparsity in linear models and have extended it to create compact neural networks in meta-learning. Different from previous studies, we use multi-layer non-linear neural networks to find sparse groups for input variables. We propose a new loss function to regularize parameters for grouped input variables, design a new optimization algorithm for this loss function, and test these methods in three real-world settings. We achieve group sparsity for three datasets, maintaining satisfying results while excluding one nucleotide position from an RNA splicing experiment, excluding 89.9% of stimuli from an eye-tracking experiment, and excluding 60% of image rows from an experiment on the MNIST dataset

Multimodal Biometrics Fusion Using Correlation Filter Bank

In this paper, a novel class-dependence feature analysis method based on Correlation Filter Bank (CFB) technique for effective multimodal biometrics fusion at the feature level is developed. In CFB, the unconstrained correlation filter trained for a specific modality is designed by optimizing the overall original correlation outputs. Therefore, the differences between modalities have been taken into account and useful information in various modalities is fully exploited. Preliminary experimental results on the fusion of face and palmprint biometrics show the superiority of the novel method. 1