Unsupervised Pre-Trained, Texture Aware and Lightweight Model for Deep Learning Based Iris Recognition Under Limited Annotated Data

In this paper, we present a texture aware lightweight deep learning framework for iris recognition. Our contributions are primarily three fold. Firstly, to address the dearth of labelled iris data, we propose a reconstruction loss guided unsupervised pre-training stage followed by supervised refinement. This drives the network weights to focus on discriminative iris texture patterns. Next, we propose several texture aware improvisations inside a Convolution Neural Net to better leverage iris textures. Finally, we show that our systematic training and architectural choices enable us to design an efficient framework with upto 100× fewer parameters than contemporary deep learning baselines yet achieve better recognition performance for within and cross dataset evaluations.