Post-Mortem Iris Recognition Resistant to Biological Eye Decay Processes

TL;DR

This paper introduces a novel iris recognition method tailored for post-mortem samples, leveraging deep learning and iris-specific kernels, achieving superior accuracy in forensic applications involving deceased individuals.

Contribution

It presents the first dedicated post-mortem iris verification method using CNNs and Siamese networks, significantly improving recognition performance over existing methods.

Findings

Outperforms existing iris recognition methods on post-mortem data

Effective across various time horizons since death

Provides open access to models and methodology

Abstract

This paper proposes an end-to-end iris recognition method designed specifically for post-mortem samples, and thus serving as a perfect application for iris biometrics in forensics. To our knowledge, it is the first method specific for verification of iris samples acquired after demise. We have fine-tuned a convolutional neural network-based segmentation model with a large set of diversified iris data (including post-mortem and diseased eyes), and combined Gabor kernels with newly designed, iris-specific kernels learnt by Siamese networks. The resulting method significantly outperforms the existing off-the-shelf iris recognition methods (both academic and commercial) on the newly collected database of post-mortem iris images and for all available time horizons since death. We make all models and the method itself available along with this paper.