Master's Thesis from the year 2016 in the subject Computer Science - Technical Computer Science, grade: 81, language: English, abstract: The goal of this thesis is to propose a fast and accurate iris pattern recognition system based on wireless network system. This thesis presents three parts; in the first part, Libor Masek algorithm is enhanced to achieve higher recognition rate. Another method of iris pattern recognition is proposed which named genetic algorithm. The two used iris pattern recognition methods are compared according to their accuracy and execution time. When testing persons of the Chinese Academy of Sciences Institute of Automation (CASIA) database, both methods achieved 100% recognition rates because there is at least one image sample for each person, which is correct matched and there is no person that is false matched. But when testing image samples per persons of CASIA database, the genetic algorithm achieved higher recognition rates and lower error rates than Libor Masek algorithm. It has been found, that the recognition time of genetic algorithm is less than Masek algorithm. The second part presents an iris image compression/decompression by using Principal Component Analysis (PCA) for compression process and Inverse Principal Component Analysis (IPCA) for decompression process. It has been proven that PCA is the most suitable method for compressing iris images because of its ability to reduce their size while maintaining the good quality of the reconstructed images. Reconstructed images using IPCA have low compression ratios (CRs) and high Peak to Signal Ratios (PSNRs), which leads to good quality. For more security, a multi-stage image compression is performed in order to protect network's transmitted data from hackers because hackers cannot guess how much the image has been compressed. The third part, includes wireless network system consisting of one central Personal Computer (PC) and four Personal Computers (PCs) that communicat
