ANFFractalNet: Adaptive neuro-fuzzy FractalNet for iris recognition

During the past few years, iris recognition is a trending research topic owing to its broad security applications from airports to homeland security border control. Nevertheless, because of the maximum cost of tools and several shortcomings of the module, iris recognition failed to apply in real life on large-scale applications. Moreover, the segmentation methods of the iris region are tackled with more issues like invalid off-axis rotations, and non-regular reflections in the eye region. To address this issue, iris recognition enabled ANFFractalNet is designed. In this investigation, Kuwahara Filter and RoI extraction are employed to pre-process an image. Moreover, the Daugman Rubber sheet model is considered for segmenting pre-processed images and then feature extraction is performed to reduce the dimensionality of data. Hence, in this framework, the iris recognition is performed utilizing the module named ANFFractalNet. Furthermore, the efficacy of ANFFractalNet utilized some analytic metrics namely, Accuracy, FAR, FRR, and loss obtained effectual values of 91.594%, 0.537%, 2.482%, and 0.084%.