Overview

Most of the works done in the area of Iris recognition systems emphasizes only on matching of the patterns with the stored templates. Security aspects of the system are still unexplored. The available security algorithms provide only some cryptographic solutions that keep the template database in a secret cryptographic form. Some recent works on fake Iris detection have done but they still lacking of efficiently detect whether an iris is real or scanned image / printed image or digital image of high resolution. &XUUHQWO\DYDLODEOHWHFKQRORJLHVFDQ¶WGHWHFWVWKDWZKHWKHUWKHLULVLPDJHSUHVHQWHGIRU authentication is really of a live person or it is of a unconscious or dead person. This is a major security threat. We successfully worked on these security issues of present iris recognition system and enhanced the detection capabilities of fake iris images .We designed and developed an intelligent iris security algorithm Iris+ for making the existing iris authentication system more secure and intelligent which can prevent users from intruders malignant scammers and hackers. We use natural motion detection algorithm for detection of natural eye movement (Blinking of eyes and left ±right movements) and reflection detection algorithm to detect reflection from retina which not occurred in fake scanned images of iris. This enhanced significantly the performance of the system in terms of security and reliability. We successfully attained 99.98% accuracy at 5.2% threshold value at 10.5 cm distance from the Iris scanner. The result is 1.41% improved over the existing Xiaofu method for fake iris detection method. Further our method is able to detect and differentiate between a live and unconscious person who could not detected in the earlier methods. We used 2-D Fourier spectra with iris image quality assessment based upon reflection. Further we used motion detection based upon frame difference of iris images and reflection detection by using Gabor filter algorithm. Our result is more reliable and secure than Czajka Method which gives +0.4 improvement over Czajka method. Our method can detect at much lesser speed 100 ms than the 40 ms of Czajka method. For higher speed detection a high rate of frame analysis is needed which consumes higher CPU cycles and consumes much memory.

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