EMiCoAReNet: An Effective Iris Recognition Using Emerging Mixed Convolutional and Adaptive Residual Network Approach

Abstract

 Iris Recognition (IR) research has been proliferated vastly with applications in authentications and security in border controls and airports to name a few. These applications have gained significance in the use of DNNs (Deep Neural Networks). These techniques have produced excellent results in IRs surpassing humans in their benchmarked performances. However, practical applications often have to process eye images with low quality caused by various disturbances like noise resulting in low resolutions. This research work attempts to overcome this deficiency by proposing EMiCoAReNet (Emerging Mixed Convolutional and Adaptive Residual Network) scheme, which can jointly learn the feature representation and perform recognition with even low quality iris images. In the first phase of work rotation, cropping, rotation after cropping, flipping, Color space transformations and Translation data augmentation techniques are performed to produce more possible execution likely images and further IFE (Iris Feature Extraction) is performed using modified GF (Gabor Filter) called EFGF (Enhanced Fourier GF) filters. The proposed scheme’s accuracy is determined by an occlusion measure while training on known IR datasets namely CASIA-Iris-IntervalV4 and UBIRIS.v2 datasets. This schema can be adapted to biometric IR tasks which need robustness, scalability and accuracy.