Secured Cryptographic Key Generation From Multimodal Biometrics: Feature Level Fusion of Fingerprint and Iris

A.Jagadeesan Research scholar/Senior Lecturer/EIE Bannari Amman Institute of Technology Sathyamangalam-638 401, Tamil Nadu, India .

Dr. K.Duraiswamy Dean/Academic K.S.Rangasamy College of Technology,
Tiruchengode – 637 209, Tamil Nadu, India .

Abstract— 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 user’s biometric features into the generated key, so as to make the key unguessable to an attacker lacking significant knowledge of the user’s 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 multi-biometric template. Finally, a 256-bit secure cryptographic key is generated from the multi-biometric 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. Keywords-Biometrics; Multimodal, Fingerprint, Minutiae points; Iris; Rubber Sheet Model; Fusion; Segmentation; Cryptographic key; Chinese Academy of Sciences Institute of Automation (CASIA) iris database. I. INTRODUCTION
The necessity for reliable user authentication techniques has risen amidst of heightened issues about security and rapid progress in networking, communication and mobility [1]. The generally utilized authentication systems that regulate the entry to computer systems or secured locations are password, but it can be cracked or stolen. For that reason, biometrics has turned out to be a practicable option to traditional identification methods in several application areas [23]. Biometrics, expressed as the science of identifying an individual on the basis of her physiological or behavioral traits, seems to achieve acceptance as a rightful method for obtaining an individual’s identity [1]. Biometric technologies have established their importance in a variety of security, access control and monitoring applications. The technologies are still novel and momentarily evolving [2]. Biometric systems possess numerous advantages over traditional authentication methods, that is, 1) biometric information cannot be obtained by direct covert observation, 2) It is difficult to share and reproduce, 3) It improves user easiness by lessening the necessity to memorize long and random passwords, 4) It safeguards against repudiation by the user. Besides, biometrics imparts the same security level to all users unlike passwords and is tolerant to brute force attacks [3]. A number of biometric characteristics are being employed today, which comprises fingerprint, DNA, iris pattern, retina, ear, thermogram, face, gait, hand geometry, palm-vein pattern, smell, keystroke dynamics, signature, and voice [16, 17]. Biometric systems that generally employ a single attribute for recognition (that is., unimodal biometric systems) are influenced by some practical issues like noisy sensor data, non-universality and/or lack of distinctiveness of the biometric trait, unacceptable error rates, and spoof attacks [4]. A probable improvement, multimodal biometric systems prevail over some of these issues by strengthening the proof acquired from several sources [5] [6]. Multimodal biometric system employs two or more individual modalities, namely, gait, face, Iris and fingerprint, to enhance the recognition accuracy of conventional unimodal methods. With the use of multiple biometric modalities, it is shown that to decrease error rates, by offering extra valuable information to the classifier. Diverse characteristics can be employed by a single system or separate systems that can function on its own and their decisions may be merged together [7]. The multimodal-based authentication can aid the system in improving the security and effectiveness in comparison to unimodal biometric authentication, and it might become challenging for an adversary to spoof the system owing to two individual biometric

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