Iris Recognition Using Principal Component Analysis

The iris recognition is a kind of the biometrics technologies based on the physiological characteristics of human body, compared with the feature recognition based on the fingerprint, palm-print, face and sound etc, the iris has some advantages such as uniqueness, stability, high recognition rate, and non-infringing etc. The iris recognition is using principal component analysis can produce spatially global features. In this paper we use the feature extraction algorithm based on PCA fo2r a compact iris code. Here for segmentation of iris image is based on Daugman’s method using integro differential operator finally, sorts the different iris patterns by improved Euclidean distance method and gives the recognition results.

We use PCA method to generate optimal basis elements which could represent iris signals efficiently. In practice the coefficient of this method are used as feature vectors. Then iris feature vectors are encoded into the iris code for storing and comparing individual's iris patterns.