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

• URL: http://arxiv.org/abs/2002.09048v1
• Date: Thu, 20 Feb 2020 22:30:38 GMT
• Title: Unsupervised Pre-trained, Texture Aware And Lightweight Model for Deep Learning-Based Iris Recognition Under Limited Annotated Data
• Authors: Manashi Chakraborty, Mayukh Roy, Prabir Kumar Biswas, Pabitra Mitra
• Abstract summary: We present a texture aware lightweight deep learning framework for iris recognition. To address the dearth of labelled iris data, we propose a reconstruction loss guided unsupervised pre-training stage. Next, we propose several texture aware improvisations inside a Convolution Neural Net to better leverage iris textures.
• Score: 17.243339961137643
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 100X fewer parameters than contemporary deep learning baselines yet achieve better recognition performance for within and cross dataset evaluations.

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