Iris recognition model based on Principal Component analysis and 2 level Haar wavelet transform: Case study CUHK and UTIRIS iris databases

A biometric recognition system provide automatic identification of human being based on some special and unique physical or behavioral features of the individual. One of the most reliable identification system is iris recognition system. This work aim to recognize and identify iris among many of images that have been save in databases. Each one of database that used manipulating in many steps starting with enhance the details of iris and segment the iris and pupil then extract the raw features based on 2D Haar wavelet transform to capture both global and local features of iris image. After that by Appling reduction step to select only the useful and unique features that belong to each person. In this work PCA used as a reduction method. Finally the minimum distance are used to check the similarity between the database’s features training set and input image, also three similarity techniques are used between input iris image and the template that save in database. Weighted Euclidean distance, Hamming distance and Cosine distance. Cosine method achieve good result than other method by using reduction and without reduction.