ICTACT Journal on Image and Video Processing

A. Muthukumar ¹, C. Kasthuri ², S. Kannan ³

Affiliations
1 Department of Electronics and Communication Engineering, Kalasalingam University, IN
2 Department of Electronics and Communication Engineering, Madurai Institute of Engineering and Technology, IN
3 Department of Electrical and Electronics Engineering, Kalasalingam University, IN

Abstract

In general, the identification and verification are done by passwords, pin number, etc., which is easily cracked by others. In order to overcome this issue biometrics is a unique tool for authenticate an individual person. Nevertheless, unimodal biometric is suffered due to noise, intra class variations, spoof attacks, non-universality and some other attacks. In order to avoid these attacks, the multimodal biometrics i.e. combining of more modalities is adapted. In a biometric authentication system, the acceptance or rejection of an entity is dependent on the similarity score falling above or below the threshold. Hence this paper has focused on the security of the biometric system, because compromised biometric templates cannot be revoked or reissued and also this paper has proposed a multimodal system based on an evolutionary algorithm, Particle Swarm Optimization that adapts for varying security environments. With these two concerns, this paper had developed a design incorporating adaptability, authenticity and security.

Keywords

Multibiometric, Cryptosystem, Score Level Fusion, PSO Algorithm, Feature Extraction.

Full Text

     

A. Muthukumar ¹, S. Kannan ²

Affiliations
1 Department of Electronics and Communication Engineering, Kalasalingam University, IN
2 Department of Electrical and Electronics Engineering, Kalasalingam University, IN

Abstract

In general, the identification and verification are done by passwords, pin number, etc., which is easily cracked by others. Biometrics is a powerful and unique tool based on the anatomical and behavioral characteristics of the human beings in order to prove their authentication. This paper proposes a novel recognition methodology of biometrics named as Finger Knuckle print (FKP). Hence this paper has focused on the extraction of features of Finger knuckle print using Scale Invariant Feature Transform (SIFT), and the key points are derived from FKP are clustered using K-Means Algorithm. The centroid of K-Means is stored in the database which is compared with the query FKP K-Means centroid value to prove the recognition and authentication. The comparison is based on the XOR operation. Hence this paper provides a novel recognition method to provide authentication. Results are performed on the PolyU FKP database to check the proposed FKP recognition method.

Keywords

Biometric, SIFT Algorithm, Feature Extraction, K-Means Algorithm.

Full Text