Texture aware autoencoder pre-training and pairwise learning refinement for improved iris recognition

Abstract

This paper presents a texture aware end-to-end trainable iris recognition system. We build upon our previous stagewise learning framework but present two key contributions: a) we propose a better autoencoding framework with a data relation loss between Gram matrix representations of input and reconstructed images. The data relation loss enables learning better texture representation which is pivotal for a texture rich dataset such as iris. Robustness of auto-encoding is further enhanced with an auxiliary denoising task. b) we design a pairwise learning architecture which subsumes the task of iris matching inside the training pipeline itself and results in significant improvement in matching performance compared to usual offline matching paradigm. On ND-IRIS-0405, CASIA.v4-Interval and IITD iris datasets our proposed model achieves better matching performance over both traditional baselines and recent deep learning paradigms. Specifically, our method yields a relative improvement of 42.30%, 64.92% and 20% in terms of equal error rates (EER) with respect to the best competing deep learning method on the respective datasets.