Post-mortem Iris Recognition with Deep-Learning-based Image Segmentation
Authors:
- Mateusz Trokielewicz,
- Adam Czajka,
- Piotr Maciejewicz
Abstract
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 hours 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.
- Record ID
- WUT83a09b86f3004ac9b42edd10bd139b66
- Author
- Journal series
- Image and Vision Computing, ISSN 0262-8856, e-ISSN 1872-8138
- Issue year
- 2020
- Vol
- 2020
- No
- 94
- Pages
- 1-11
- Publication size in sheets
- 5193.30
- Article number
- 103866
- Keywords in English
- Biometrics; Iris recognition; Post-mortem; Image segmentation
- ASJC Classification
- DOI
- DOI:10.1016/j.imavis.2019.103866 Opening in a new tab
- URL
- https://www.sciencedirect.com/science/article/pii/S0262885619304597?via%3Dihub Opening in a new tab
- Language
- (en) English
- File
-
- File: 1
- Trokielewicz i in IVC 2020.pdf
-
- Not used for evaluation
- yes
- Score (nominal)
- 0
- Publication indicators
- = 15; = 3; : 2018 = 1.541; : 2020 (2 years) = 2.818 - 2020 (5 years) =3.069
- Citation count
- 38
- Uniform Resource Identifier
- https://repo.pw.edu.pl/info/article/WUT83a09b86f3004ac9b42edd10bd139b66/
- URN
urn:pw-repo:WUT83a09b86f3004ac9b42edd10bd139b66
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or PerishOpening in a new tab system.