Iris Feature Detection Using Split Block And PSO For Iris Identification System

The past decade has seen the rapid development of iris identification in many approaches to identify unique iris features such as crypts. However, it is noted that, unique iris features change due to iris aging, diet or human health conditions. The changing of iris features creates the mismatch in comparison phase to determine either genuine or not genuine. Therefore, to determine genuinely, this study proposes a new model of iris recognition using combinational approach of a split block and particle swarm optimization (PSO) in selecting the best crypt among unique iris features template. The split block has been used in this study to separate the image with the part that very important in the iris template meanwhile, the particles in PSO searches the most optimal crypt features in the iris. The results indicate an improvement of PSNR rates, which is 23.886 dB and visually improved quality of crypts for iris identification. The significance of this study contributes to a new method of feature extraction using bio-inspired, which enhanced the ability of detection in iris identification

Non-ideal iris recognition

Of the many biometrics that exist, iris recognition is finding more attention than any other due to its potential for improved accuracy, permanence, and acceptance. Current iris recognition systems operate on frontal view images of good quality. Due to the small area of the iris, user co-operation is required. In this work, a new system capable of processing iris images which are not necessarily in frontal view is described. This overcomes one of the major hurdles with current iris recognition systems and enhances user convenience and accuracy. The proposed system is designed to operate in two steps: (i) preprocessing and estimation of the gaze direction and (ii) processing and encoding of the rotated iris image. Two objective functions are used to estimate the gaze direction. Later, the off-angle iris image undergoes geometric transformations involving the estimated angle and is further processed as if it were a frontal view image. Two methods: (i) PCA and (ii) ICA are used for encoding. Three different datasets are used to quantify performance of the proposed non-ideal recognition system