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Post-Mortem Human Iris Segmentation Analysis with Deep Learning

Iris recognition is widely used in several fields such as mobile phones, financial transactions, identification cards, airport security, international border control, voter registration for living persons. However, the possibility of identifying deceased individuals based on their iris patterns has emerged recently as a supplementary or alternative method valuable in forensic analysis. Simultaneously, it poses numerous new technological challenges and one of the most challenging among them is the image segmentation stage as conventional iris recognition approaches have struggled to reliably execute it. This paper presents and compares Deep Learning (DL) models designed for segmenting iris images collected from the deceased subjects, by training SegNet and DeepLabV3+ semantic segmentation methods where using VGG19, ResNet18, ResNet50, MobileNetv2, Xception, or InceptionResNetv2 as backbones. In this study, our experiments demonstrate that our proposed method effectively learns and identifies specific deformations inherent in post-mortem samples and providing a significant improvement in accuracy. By employing our novel method MobileNetv2 as the backbone of DeepLabV3+ and replacing the final layer with a hybrid loss function combining Boundary and Dice loss, we achieve Mean Intersection over Union of 95.54% on the Warsaw-BioBase-PostMortem-Iris-v1 dataset. To the best of our knowledge, this study provides the most extensive evaluation of DL models for post-mortem iris segmentation.

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