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In this paper, we present a texture aware lightweight deep learning frameworkfor iris recognition. Our contributions are primarily three fold. Firstly, toaddress the dearth of labelled iris data, we propose a reconstruction lossguided unsupervised pre-training stage followed by supervised refinement. Thisdrives the network weights to focus on discriminative iris texture patterns.Next, we propose several texture aware improvisations inside a ConvolutionNeural Net to better leverage iris textures. Finally, we show that oursystematic training and architectural choices enable us to design an efficientframework with upto 100X fewer parameters than contemporary deep learningbaselines yet achieve better recognition performance for within and crossdataset evaluations.

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