EFFICIENT BIOMETRIC IRIS RECOGNITION USING GAMMA CORRECTION & HISTOGRAM THRESHOLDING WITH PCA

In this paper, a new Iris Recognition method is presented. An Iris Recognition system acquires a human eye image, segments the Iris region from the rest of the image, normalizes this segmented image and encodes features to get a compact iris template. Performance of all subsequent stages in an Iris Recognition system is highly dependent on correct detection of boundaries in the eye images. In this paper, we present one such system which finds boundary using images. We propose “Iris Recognition for biometric recognition using Gamma correction & Histogram Thresholding with PCA”. Iris biometric has created vital progress over past decade among the all biometric trains. The white region of eye is sclera, which is exposed. The sclera is roofed by the thin clear wet layer referred as conjunctiva. Conjunctiva and episclera contains the blood vessels. Our aim is to segment the sclera patterns from the eye footage. The segmented iris region was normalized to minimize the dimensional inconsistencies between iris regions. Most of biometric recognition algorithms employ computer vision, pattern recognition and image processing techniques or their combination. On the other hand, our approach using image matching is based on gamma correction with histogram thresholding technique. This paper focuses on the detection of Iris region from the eye image, enhancement of blood vessels and feature extraction using gamma correction. The features extracted from Iris regions are used for biometric recognition. The experimental results provide significant improvement in the segmentation accuracy. For the implementation of this proposed work we use the Image Processing Toolbox under Matlab software