Interpretable Deep Learning-Based Forensic Iris Segmentation and Recognition

• URL: http://arxiv.org/abs/2112.00849v1
• Date: Wed, 1 Dec 2021 21:59:16 GMT
• Title: Interpretable Deep Learning-Based Forensic Iris Segmentation and Recognition
• Authors: Andrey Kuehlkamp, Aidan Boyd, Adam Czajka, Kevin Bowyer, Patrick Flynn, Dennis Chute, Eric Benjamin
• Abstract summary: We present an end-to-end deep learning-based method for postmortem iris segmentation and recognition. The method was trained and validated with data acquired from 171 cadavers, kept in mortuary conditions, and tested on subject-disjoint data acquired from 259 deceased subjects.
• Score: 4.691925709249742
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Iris recognition of living individuals is a mature biometric modality that has been adopted globally from governmental ID programs, border crossing, voter registration and de-duplication, to unlocking mobile phones. On the other hand, the possibility of recognizing deceased subjects with their iris patterns has emerged recently. In this paper, we present an end-to-end deep learning-based method for postmortem iris segmentation and recognition with a special visualization technique intended to support forensic human examiners in their efforts. The proposed postmortem iris segmentation approach outperforms the state of the art and in addition to iris annulus, as in case of classical iris segmentation methods - detects abnormal regions caused by eye decomposition processes, such as furrows or irregular specular highlights present on the drying and wrinkling cornea. The method was trained and validated with data acquired from 171 cadavers, kept in mortuary conditions, and tested on subject-disjoint data acquired from 259 deceased subjects. To our knowledge, this is the largest corpus of data used in postmortem iris recognition research to date. The source code of the proposed method are offered with the paper. The test data will be available through the National Archive of Criminal Justice Data (NACJD) archives.

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