Ocular Detection for Biometric Recognition Using Probablastic Component Analysis
Bhupinder Pal Singh, Taranpreet Kaur · 2014
P Abstract: Ocular images processing is an important task in: i) biometrics system based on retina and/or sclera images, and ii) in clinical ophthalmology diagnosis of diseases like various vascular disorders. Ocular 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. This paper focuses on the detection of ocular region from the eye image, enhancement of blood vessels and feature extraction. The features extracted from ocular 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.Keywords: Ocular pattern, sclera and conjunctival vasculature, ocular detection, biometrics, iris segmentation, growing based segmentation; Gabor filter, PCA, Codification, Normalization, Image Processing.