This paper discusses the results of a study that aimed to develop an eigenface technique known as (PC) 2A that collect the image of the original face with its vertical and horizontal projections. The basic components of the image were analyzed in the image enrichment section. An evaluation of the proposed method demonstrates that it costs less than the standard eigenface technique. Moreover, the experimental results show that a front-end database that has a gray level for each person has one training image; thus, in terms of accuracy, it was possible to get a 3-5% result for the proposed (PC)2A, which is higher than the precision of the standard eigenface technique. The main objective of this paper is to demonstrate the weaknesses and strengthens of the facial recognition approach as an identifier known as eigenfaces. This aim was achieved by using the principal components analysis algorithm based on the images of previously stored training data. The outcomes show the strength of the proposed technique, in which it was possible to obtain accuracy results of up to 96%, which in turn provides support for developing the technique proposed in this paper in the future because this work is of great importance in the field of biological treatments, the need for which has significantly increased over the last 5 years.

Encryption technology is one of the main means of protecting information security and includes many forms in terms of digital signatures, authentication, system security, confidentiality and data integrity and other functions. Therefore, Encryption and decryption are the best solution to protect data, especially not only sensitive data and protect them from attackers who aim to steal information. In this paper we use asymmetric encryption algorithms such as (RSA, Elliptic Curve, NTRU) that are used to send data over the network to provide security to users. We work on a review of the algorithms mentioned because of their importance in the encryption side and will give a summary of the performance of its work.

Vein recognition systems are a form of biometric recognition that can distinguish people according to their vascular structure. Identification from hand-wrist vein pattern is one of these systems. In this study, hand-wrist vein images taken from people using an infrared light source with a wavelength of 850 nm were segmented by passing through various image processing algorithms. Scale-independent feature transformation (SURF) method was used for key point extraction from segmented images. The features obtained by the SURF method are rotation, camera angle, ambient light intensity, etc. This method has been preferred because it is invariant against situations. In the identification process, the Euclidean distance method was used by making use of the extracted key points. The accuracy rate was determined as 97% as a result of the matching processes using the hand-wrist vein patterns in the database.

The article is devoted to real-time algorithms for detecting events described by four scenarios: movement in a forbidden direction, being in a sterile zone, leaving (stealing) an object, throwing an object. The main idea of the algorithms is the analysis of the trajectories of moving objects, for which two different approaches are proposed in the article.

Security is one of the main sources of information protection, especially sensitive information that is transmitted over the Internet. Encryption is one of the most important elements used, which is an effective and necessary element to provide high-level security communication between different entities by transmitting unclear and encrypted information that does not allow unauthorized person to access, the method of choosing the appropriate and correct encryption algorithm is important to provide a secure connection that provides a more efficient and accurate encryption system. In this paper, we will review the algorithms (Triple DES, AES & HiSea) for secret key encryption that are most commonly used for this type of encryption.