Biometric Hand Recognition by Application of Particle Swarm Optimization Method

the field of biometrics research continues to advance, and although many identification and verification methods have been established, their effectiveness is still insufficient due to the increasing demands on security. For the purposes of biometric identification or authentication applications, hand biometric recognition has been effectively established. Multiple biometric methods are utilized since using only one biometric method often decreases the systems' confidence. In the present article, we suggest a multi-biometric fusion method for person recognition. In fact, we employ two different forms of biometrics: the hand's shape and palm print. The texture was extracted using discrete cosine transform (DCT) method. Subsequently, PSO algorithm is employed in the process of identification to integrate the geometric properties of the hands with the texture of the examined palm. Simulation results demonstrate the key benefits of this strategy with a realization rate of over 85% for a population of 230 people.