Fingerprint recognition based on shark smell optimization and genetic algorithm

Fingerprint recognition is a dominant form of biometric due to its distinctiveness. The study aims to extract and select the best features of fingerprint images, and evaluate the strength of the Shark Smell Optimization (SSO) and Genetic Algorithm (GA) in the search space with a chosen set of metrics. The proposed model consists of seven phases namely, enrollment, image preprocessing by using weighted median filter, feature extraction by using SSO, weight generation by using Chebyshev polynomial first kind (CPFK), feature selection by using GA, creation of a user’s database, and matching features by using Euclidean distance (ED). The effectiveness of the proposed model’s algorithms and performance is evaluated on 150 real fingerprint images that were collected from university students by the ZKTeco scanner at Sulaimani city, Iraq. The system’s performance was measured by three renowned error rate metrics, namely, False Acceptance Rate (FAR), False Rejection Rate (FRR), and Correct Verification Rate (CVR). The experimental outcome showed that the proposed fingerprint recognition model was exceedingly accurate recognition because of a low rate of both FAR and FRR, with a high CVR percentage gained which was 0.00, 0.00666, and 99.334%, respectively. This finding would be useful for improving biometric secure authentication based fingerprint. It is also possibly applied to other research topics such as fraud detection, e-payment, and other real-life applications authentication.