Efficient iris recognition system based on dual boundary detection using robust variable learning rate Multilayer Feed Forward neural network

This paper presents a novel approach towards iris recognition based on dual boundary (Pupil-Iris & Sclera-Iris) detection and then using a modified Multilayer Feed Forward neural network (MFNN) to perform an efficient automatic classification. The novelty of the work resides in the fact that the proposed method features the localization of the dual iris boundaries to be used as feature vector for classification. The process of information extraction starts by preprocessing the eye-image to remove specular highlight and then locating the pupil of the eye by using edge detection. The centroid of the detected pupil is chosen as the reference point for extracting the boundary points. The boundary points are recorded using radius vector functions approach. The proposed feature vector is obtained by concatenating the contour points of the Pupil-Iris boundary and the Sclera-Iris boundary which will yield a unique pattern named as Iris signature. The proposed method is translational and scale invariant. The classification is performed using the MFNN via a modified version of back-propagation algorithm which uses a time varying learning rate. The proposed system has been tested on moderate no of pictures taken from MMU iris database in the presence of additive noise for different values of signal-to-noise ratio (SNR). Experimental result for percentage recognition shows that the proposed method outperforms the single boundary method.