Analysis & Performance Evolution of IRIS Recognition SVD, KLV and EBP Algorithms using Neural Network Classifier

ABSTRACT Iris recognition, a relatively new biometric technology, has great advantages, such as variability, stability and security, thus it is the most promising for high security environments. Hence numbers of IRIS recognition algorithms are available, techniques we purposed such as SVD (singular value decomposition),Characterizing key local variation (KLV) this technique will be used to extract the features of iris.The non-useful information such as sclera, pupil, eyelashes and eyelids are removed and Region of Interest (ROI) is extracted. Then the Iris template is generated to reduce the information thereby concentrating only on the ROI. The details of this combined system named as Iris Pattern recognition using neural Network Approach. All IRIS recognition algorithms are evaluated on the basis of classification rate obtained after changing different parameters such as number of classes, Training and Testing patterns, SVD: To reduce complexity of layered neural network the dimension of input vectors are optimized using Singular Value Decomposition (SVD).An efficient algorithm describes for iris recognition by characterizing key local variations. Local details of the iris generally spread along the radial direction in the original image corresponding to the vertical direction in the normalized image. Therefore information density in the angular direction corresponding to the horizontal direction in the normalized image is much higher than that in other directions i.e., it may suffice only to capture local sharp variations along the horizontal direction in the normalized image to characterize an iris. A local extremum is either a local minimum or a local maximum. The optimum classification values are obtained with SVD 20 dimension and maximum number of classes as 9 with the state-of-the art computational resources. The details of this combined system named as SVD-EBD system for Iris pattern recognition.