Impact of environmental factors on biometric matching during human decomposition

Bolme, David S.; Tokola, Ryan; Boehnen, Christopher; Saul, Tiffany B.; Sauerwein, K.; Dawnie, Wolfe

doi:10.1109/btas.2016.7791177

Cited by 24 publications

(21 citation statements)

References 14 publications

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“…Bolme et al [4] tracked biometric capabilities of face, fingerprint and iris during human decomposition in outdoor conditions. Twelve subjects were placed in a body farm to assess how the environment and time affect performance of biometric methods.…”

Section: Review Of Post-mortem Iris Recognition Efforts To Date Amentioning

confidence: 99%

“…However, recent studies demonstrate that the dynamics of eye and iris decomposition, from the biometric point of view, are more complex and less rapid than initially believed. These processes heavily depend on the ambient conditions, but today we know that iris patterns can still be useful for recognition a few days after death in a warm environment [4], [5], or up to even a few weeks in a mortuary environment [6]- [8].…”

Section: Introductionmentioning

confidence: 99%

“…Although this survey is focused on iris, the face and fingerprint modalities also offer potential postmortem biometric recognition utility. Some preliminary studies [4], [5] on automated recognition for postmortem face and fingerprint explored the length-of-viability of the trait when left exposed to the natural elements. This of course varies with temperature, humidity and season.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Post-Mortem Iris Recognition—A Survey and Assessment of the State of the Art

et al. 2020

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Post-mortem biometrics entails utilizing the biometric data of a deceased individual for determining or verifying human identity. Due to fundamental biological changes that occur in a person's biometric traits after death, post-mortem data can be significantly different from ante-mortem data, introducing new challenges for biometric sensors, feature extractors and matchers. This paper surveys research to date on the problem of using iris images acquired after death for automated human recognition. A comprehensive review of existing literature is complemented by a summary of the most recent results and observations offered in these publications. This survey is unique in several elements. Firstly, it is the first publication to consider iris recognition where gallery images are acquired before death (perimortem images) and the probe images are acquired after death from the same subjects. Secondly, results are presented from the largest database of peri-mortem and post-mortem iris images, collected from 213 subjects by two independent institutions located in the U.S. and Poland. Thirdly, post-mortem recognition viability is assessed using more than 20 iris recognition algorithms, ranging from the classic (e.g., Gabor filteringbased) to the modern (e.g., deep learning-based). Finally, we provide a medically informed commentary on post-mortem iris, analyze the reasons for recognition failures, and identify key directions for future research.

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Section: Review Of Post-mortem Iris Recognition Efforts To Date Amentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Post-Mortem Iris Recognition—A Survey and Assessment of the State of the Art

et al. 2020

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“…A study tracking biometric capabilities of both the typically used forensic modalities, namely fingerprints and faces, as well as iris is presented by Bolme et al [5]. The authors placed twelve deceased subjects in outdoor conditions to assess how the environment and time affect the biometric performance.…”

Section: Related Workmentioning

confidence: 99%

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

Trokielewicz

Czajka

Maciejewicz

2020

Image and Vision Computing

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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.

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“…Due to the difficulties in data collection and the obvious unpleasantness of such experiments, very little research has been published, especially when human eyes are concerned, with few exceptions, namely (A. Sansola, 2015) (the paper concludes that post-mortem iris recognition works fine in about 80% of the cases for samples acquired up to 2 days after death) and (Bolme et al, 2016) (which mostly focuses on post-mortem face and fingerprint recognition, with few conclusions regarding irises). (Saripalle et al, 2015) present a study of post-mortem iris recognition using cadaver eyes of a domestic pig, reporting that the eyes lose their capability to serve as a biometric identifier in 6 to 8 hours post-mortem.…”

Section: Post-mortem Iris Recognitionmentioning

confidence: 99%

Iris Recognition under Biologically Troublesome Conditions - Effects of Aging, Diseases and Post-mortem Changes

Trokielewicz

Czajka

Maciejewicz

2017

Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies

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This paper presents the most comprehensive analysis of iris recognition reliability in the occurrence of various biological processes happening naturally and pathologically in the human body, including aging, illnesses, and post-mortem changes to date. Insightful conclusions are offered in relation to all three of these aspects. Extensive regression analysis of the template aging phenomenon shows that differences in pupil dilation, combined with certain quality factors of the sample image and the progression of time itself can significantly degrade recognition accuracy. Impactful effects can also be observed when iris recognition is employed with eyes affected by certain eye pathologies or (even more) with eyes of the deceased subjects. Notably, appropriate databases are delivered to the biometric community to stimulate further research in these utterly important areas of iris biometrics studies. Finally, some open questions are stated to inspire further discussions and research on these important topics. To Authors' best knowledge, this is the only scientific study of iris recognition reliability of such a broad scope and novelty. a a Manuscript accepted for publication at the BIOSIGNALS 2017 conference

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Impact of environmental factors on biometric matching during human decomposition

Cited by 24 publications

References 14 publications

Post-Mortem Iris Recognition—A Survey and Assessment of the State of the Art

Post-Mortem Iris Recognition—A Survey and Assessment of the State of the Art

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

Iris Recognition under Biologically Troublesome Conditions - Effects of Aging, Diseases and Post-mortem Changes

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