UNCONSTRAINED IRIS AUTHENTICATION THROUGH FUSION OF RGB CHANNEL INFORMATION
Abstract
The unique iris pattern of each human eye is complex, but easily be scanned or captured by a camera. However, the high cost infrared iris scanners used for acquisition causes inconvenience to users by distance related constraints. This restricts its widespread use in real-time applications such as airports and banks. The images captured by cameras under visible wavelength are obstructed by the presence of reflections and shadows which requires additional attention. The main objective of this paper is to propose a secure biometric iris authentication system by fusion of RGB channel information from the real-time data captured under visible wavelength and varying light conditions. The proposed system is adapted to a real-time noisy iris dataset. The effectiveness of this proposed system was tested on two different color iris datasets, namely, a public database UBIRISv1 and a newly created database SSNDS which contains images captured with any digital/mobile camera of minimum 5MP under unconstrained environments. This system supports the cross sensor acquisition and successful iris segmentation from these unconstrained inputs. The features from each channel are extracted using log Gabor filter and a matching is performed using hamming distance based on two thresholds (inter and intra class variations). The performance quality of the proposed biometric system leads to the feasibility of a new cost-effective approach for any real-time application, which requires authentication to ensure quality service, enhance security, eliminate fraud, and maximize effectiveness.
