Non-Cooperative Iris Recognition: A Novel Approach for Segmentation and Fake Identification

Abstract

Iris recognition, the ability to recognize and distinguish individuals by their pattern, is the most reliable biometric in terms of recognition and identification performance. However, performance of these systems is affected by the heterogeneous images (regarding focus, contrast, or brightness) and with several noise factors (iris obstruction and reflection) when the cooperation is not expectable from the subject. Current Iris recognition system does not deal with the noise data and substantially increase their error rates in these conditions. The non-cooperative iris segmentation takes a vital role in human identification system. This can be simplified with the help of canny edge detection as well as Cartesian to polar conversion methods. An Iris classification method is proposed on the segmented and normalized iris image that divides the image into six regions, followed by independent feature extraction in each region. This will provide the iris signature in terms of binary values, then that are compared with each region for the identification. In addition to this Fake identification is also done in this study. Fake, the original image is forged by fixing lenses over the iris portion

A Novelty Approach of Spatial Co-Occurrence and Discrete Shearlet Transform Based Texture Classification Using Lpboosting Classifier

“…This method is compared in the digital texture images provided by Carr and De Miranda (1998). The effective approach for feature comparison in Iris database to distinguish the various patterns of iris with reduced error rate as well as the fake identification in biometric applications delivered by Kumar et al (2013). Later, the texture sea ice pattern of the Synthetic Aperture Radar (SAR) imagery are evaluated through Gray-Level Co-Occurrence Matrix (GLCM) based on the entropy textural descriptors.…”