MULTILEVEL AUTHENTICATION SYSTEM BASED ON PERIOCUALR FEATURES USING DEEP LEARNING ALGORITHM

The use of biometric for identification purposes requires that a particular biometric factor be unique for each individual that it can be calculated, and that it is invariant over time. Biometrics such as signatures, photographs, fingerprints, voiceprints and retinal blood vessel patterns all have noteworthy drawbacks. Although signatures and photographs are cheap and easy to obtain and store, they are impossible to identify automatically with assurance, and are easily forged. Human iris on the other hand as an internal organ of the eye and as well protected from the external environment, yet it is easily visible from within one meter of distance makes it a perfect biometric for an identification system with the ease of speed, reliability and automation. Iris recognition is regarded as the most reliable and accurate biometric identification system available. Iris recognition is an automated method of biometric identification that uses mathematical pattern-recognition techniques on images of the irises of an individual’s eyes, whose complex random patterns are unique. In this work it is proposed to implement a face and iris recognition system, where Grassmann algorithm, Curvelet transform and deep neural network is used to segment the face, eye and iris region. A template of the detected region is created using template matching for recognition is based on features in real time enrolment system. The results shows that the proposed method is efficient for existing iris image based biometric recognition.