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.

 Recognition of people relying on biometric characteristics is a common phenomenon in our society. It has increased in recent years in most areas of life such as government, department, companies, and banks. Fingerprint identification is one of the most common and credible personal biometric identification methods. Extracting features are one of the most important steps in the fingerprint identification; the strength of any system depends mainly on this step, where whenever the features obtained are accurate whenever the identification process is more accurate. Therefore, an effective and efficient method must be used to extract the features. This paper solved two main problems that were (1) improving security by designing and implementing an accurate, efficient, and fast authentication system for the identification and verification process using an intelligent algorithm to extract the best features from the fingerprint image and (2) evaluating the strength of the Shark Smell Optimization (SSO) in the search space with a chosen set of metrics. This paper aims to extract the best features of the fingerprint image using an algorithm that depends on nature for its movement and work; therefore, the SSO was used. In this paper, the SSO algorithm is used to extract the features. SSO is a new meta-heuristic algorithm that uses smart methods and random movements to get its prey. These methods and movements were used to extract features from the fingerprint image which will be used later for identification and verification process. The proposed method is implemented through four phases, namely, create a database to store and organize data, image pre-processing using median filter, feature extraction using SSO algorithm, and matching process using euclidean distance. The results demonstrated the strength, accurate, credible, and effectiveness of the algorithm used by applying it on (150) real fingerprint samples taken from university students, where the results of false acceptation rate, false rejection rate, and correct verification rate were 0.00, 0.00666, and 99.334%, respectively.

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Information hiding is one of the great significance in our lives today. Especially when it is sent from one place to another place (from sender to receiver) so it is necessary to find an excellent way to hide this secret information. In this paper we will use the Intelligent Water Drops Algorithm (IWDA) to find the best locations in the cover image (color image) that will be used to hide secret watermark image, this algorithm is used to find the best solutions in the search space by depending on their behavior to reach the goal quickly and efficiently, the (IWD) is nature-inspired swarm-based optimization algorithm, it is depend on the processes that occur within natural river system to find the best paths among many paths and can get-away from local optima more readily than evolutionary algorithms. The PSNR value for new objective (stego image ) it has been measured, and it was very good, where the PSNR value for the image (1) and the image (2) were 82.74 and 81.71 respectively.

Information is so important thing to us. Therefore, protecting the data by using information concealing techniques have become interest in the most applications. This paper proposed new algorithm is gravitational search algorithm in order to determine best locations in a carrier image (color image) that will be used to conceal secret information by effective and efficient method, this paper propose an effective and efficient method for determining best hiding locations in a carrier (colored image) by using gravitational search algorithm. The gravitational search algorithm is depended on gravity rules that concerns the fact that an object with mass attracts one another. The PSNR of the stego image1 and stego image2 are 72.55 and 71.21 respectively.