The security of message information has drawn more attention nowadays, so; cryptography has been used extensively. This research aims to generate secured cipher keys from retina information to increase the level of security. The proposed technique utilizes cryptography based on retina information. The main contribution is the original procedure used to generate three types of keys in one system from the retina vessel's end position and improve the technique of three systems, each with one key. The distances between the center of the diagonals of the retina image and the retina vessel's end (diagonal center-end (DCE)) represent the first key. The distances between the center of the radius of the retina and the retina vessel's end (radius center-end (RCE)) represent the second key. While the diagonal-radius center and the retina vessel's end (diagonal-radius center-end (DRCE)) represent the third key. The results illustrate the process's validity and applicability. Also, improve the time required to decrypt the cipher-text by a brute force attack (BFA) from (4.358e+139) year in the compared technique to (1.3074e+140) year for retina3. The BFA time will increase with increasing the number of retina vessels, as in retina1, 2, and 3, which have 24, 53, and 103 retina vessels.

Wireless Sensor Networks (WSNs) consists of a huge number of tiny, low-priced, and battery-powered devices with limited on board sensing, processing and communication capabilities. The batteries of sensor nodes of WSNs are usually with limited capacity; hence it is essential to conserve battery energy to prolonging the WSNs lifetime. Therefore, this paper deals with the matter of energy consumption minimization to maximize the overall network lifespan. In this research, a mathematical model for the lifetime of WSN is formulated based on several parameters to find out the optimal solution of the energy problem in the field of wireless sensor networks using the Modified Particle Swarm Optimization (MPSO) and Ant Colony Optimization (ACO) algorithms. The proposed system has been implemented using MATLAB 7.6.0 (R2008a) software environment. The computer simulation results show that the presented approach for power consumption minimization is faster than the previous works by 10 times, and the network lifetime is increased by at least 8 times. Furthermore, the conducted simulation indicates that the MPSO algorithm offers superior results in terms of accuracy (99.36%) in comparison with ACO algorithm (97.92%). In this regards, MPSO algorithm acts with much better efficiency as computational time minimizes, simple, has stable convergence characteristics, and designed with adaptable inertial weight and acceleration factors than ACO algorithm.

Face recognition is a complex visual classification task which plays an important role in computer vision, image processing, and pattern recognition. SMWT is proposed to extract the features in images before using the PCA and histogram based method classifiers. SMWT will be used in the recognition process to minimize the size of the data base. Since left direction poses equal to the right direction poses, and as it is a translation invariant, so it has the same data, so one can delete some images that have such direction in poses, so the size of the database will reduce. Also SMWT will be used to enhance the recognition rate. In this approach the recognition rate was94.5%byusing the Histogram Based Method, and a 63% when using (PCA), when the numbers of training and test images are both equal five images.

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