FAR and FRR based analysis of iris recognition system

A biometric system provides identification about an individual based on unique features or characteristics possessed by the individual. A good number of identification systems based on behavioral characteristics such as voice, signature, handwriting, speech, keystroke and physical characteristics (including face, finger print and iris) are being employed for identification of an individual. Among all these, Iris Recognition (IR) is considered to be most accurate and reliable. Various researchers have proposed a number of algorithms based on different feature extraction techniques for IR. IR using statistical features is one of these techniques. In this paper, two different types of statistical feature extraction techniques explaining cumulative sum based change analysis and explaining correlation between adjacent pixels have been implemented and compared. Major difference between these two techniques is the process of normalization. An attempt has been made to compare these two techniques using FAR analysis, FRR analysis, memory requirement and algorithmic complexity.