Fingerprint recognition based on shark smell optimization and genetic algorithm

Abstract

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.

A Secure Image Steganography Using Shark Smell Optimization and Edge Detection Technique

“…Entropy is use to assessment the amount data of an image and to contrast the difference of data of plain media and Stego media. Table (1) shows the results of the measures that were applied on five images in evaluating the proposed system. The results proved the strength and effectiveness of the proposed system The effectiveness of the suggested approach is contrasted with another approaches [43,44,45,46,47] in idioms of capacity and PSNR on 'Lena', 'Baboon', 'Pepper'' images which have been outlined in table 2 and figure (4).…”

“…With the fast development of internet and technology life [1] and advancement of image purports, the difficulty of protecting secrecy of such purports through the network has become a main apprehension in the recent years. Using effective image retrieval techniques, the intruders constantly attempt to access relevant image content [2] before launching a surprise attack using image malware [3,4].So, the field of data and multimedia secrecy has become very important and the field of its applications is extending [5].…”

Data mining techniques for lung and breast cancer diagnosis: A review

“…Several [14] an effective associative rule that used the genetic algorithm for predicting three different diseases which were diabetes, breast-cancer, and heart have been modeled. The main inspiration for using the genetic algorithm [15], [16] was to discover predictive rules that were highly understandable and given high predictive accuracy. The outcomes indicated that most of the classifier rules predicted heart disease with the highest accuracy which was 98% when compared with diabetes and breast-cancer accuracy which were 82% and 84.8%, respectively.…”

A systematic overview of secure image steganography

“…The fast technology development and automated life elevated the prerequisite for an additional security level. All over the globe, security is a growing requisite because a lack of security can result in great damage [1], [2]. The internet and technology are enhanced rapidly day by day.…”