An optimized Identification System by using Shark Smell algorithm for Biometric Images Crossing

Abstract

The security and privacy fields and multimedia biometrics have been widely used today for personal authentication. Sclera and Palm-print of humans are one of the fastest, accurate, reliable, and secure biometric techniques for identification and verification based on unique features. The majority of the biometric systems are based on the global features, which may lead to weak performance in cases of poor-quality biometric images, therefore, swarm intelligence techniques are used to improve recognition accuracy, reliability, and quickness. In this paper, an enhancement shark smell optimization (ESSO) is proposed to build an efficient hybrid identification system depend on the sclera and palm-print images. The SIFT algorithm used to extract features from the biometric images. The optimal key-points from this feature are obtained using ESSO and chaotic map, and finally, generation digital signature using a 256-MD5 algorithm for each user. The Package of the NIST tests proves that the generated keys are random, unpredictable, uncorrelated, and robust against different kinds of attacks.