Post-mortem iris recognition with deep-learning-based image segmentation

• Published in: Image and vision computing Vol. 94; p. 103866
• Main Authors: Trokielewicz, Mateusz, Czajka, Adam, Maciejewicz, Piotr
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2020
• Subjects: Biometrics; Image segmentation; Iris recognition; Post-mortem; Biometrics; Image segmentation; Post-mortem; Iris recognition
• ISSN: 0262-8856; 1872-8138; 1872-8138
• DOI: 10.1016/j.imavis.2019.103866

This paper proposes the first known to us iris recognition methodology designed specifically for post-mortem samples. We propose to use deep learning-based iris segmentation models to extract highly irregular iris texture areas in post-mortem iris images. We show how to use segmentation masks predicted by neural networks in conventional, Gabor-based iris recognition method, which employs circular approximations of the pupillary and limbic iris boundaries. As a whole, this method allows for a significant improvement in post-mortem iris recognition accuracy over the methods designed only for ante-mortem irises, including the academic OSIRIS and commercial IriCore implementations. The proposed method reaches the EER less than 1% for samples collected up to 10 hours after death, when compared to 16.89% and 5.37% of EER observed for OSIRIS and IriCore, respectively. For samples collected up to 369 h post-mortem, the proposed method achieves the EER 21.45%, while 33.59% and 25.38% are observed for OSIRIS and IriCore, respectively. Additionally, the method is tested on a database of iris images collected from ophthalmology clinic patients, for which it also offers an advantage over the two other algorithms. This work is the first step towards post-mortem-specific iris recognition, which increases the chances of identification of deceased subjects in forensic investigations. The new database of post-mortem iris images acquired from 42 subjects, as well as the deep learning-based segmentation models are made available along with the paper, to ensure all the results presented in this manuscript are reproducible. •Post-mortem-specific iris image segmentation tool•Drop-in segmentation stage replacement for typical iris recognition pipelines•New dataset of cadaver iris images (42 subjects)•Experiments showing substantial matching accuracy improvements