Iris Recognition After Death

Trokielewicz, Mateusz; Czajka, Adam; Maciejewicz, Piotr

doi:10.1109/tifs.2018.2881671

Cited by 44 publications

(49 citation statements)

References 17 publications

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“…In addition to evaluating our approach against the unmodified OSIRIS algorithm, we also choose to compare it with one of the leading iris matchers from commercial vendors, namely the IriCore [23], which offered the best performance out of four matchers in our previous evaluations of post-mortem decay impact on various iris recognition methodologies [7]. This commercial matcher was also ranked as one of the two best matchers in NIST IREX I, it has the STQC certification and is part of the world largest biometric project in India (AADHAAR).…”

Section: Iricore: a Commercial Benchmark Methodsmentioning

confidence: 99%

“…In this work, we build upon the findings presented in [10], by training several additional models with larger amounts of post-mortem data and different ground truth mask creation rules, together with constructing an iris image normalization method based on the circular Hough transform (CHT). This end-to-end segmentation algorithm is then coupled with the well known open source iris recognition method OSIRIS [11], and evaluated against the baseline OSIRIS performance and against the commercial Iri-Core method, which presented the best performance of post-mortem iris matching in past studies [7]. The contributions that this study makes towards the state-of-theart in post-mortem iris recognition are thus the following:…”

Section: Contributions Of This Workmentioning

confidence: 99%

“…Following the findings reported in [7] that strongly favor the NIR images for postmortem iris recognition purposes, we only employ this type of images in all experiments conducted in this work. Example images from each of the evaluation databases are shown in Fig.…”

Section: Databases Of Iris Imagesmentioning

confidence: 99%

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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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Section: Iricore: a Commercial Benchmark Methodsmentioning

confidence: 99%

Section: Contributions Of This Workmentioning

confidence: 99%

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

Trokielewicz

Czajka

Maciejewicz

2020

Image and Vision Computing

Self Cite

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“…Therefore, how to keep attributes from the same identity consistent, while taking full advantage of information for capturing features with multiple views are important questions for the future. Second, biometric verification [32,30,24,94,114] is a developing application for digital mobile devices to resist various attacks in the real world. Compared with full-face based biometric verification [24,30], facial attributes contain more detailed characteristics and can better facilitate active authentication.…”

Section: Model-based Methodsmentioning

confidence: 99%

“…These personal details make these devices the targets of various attacks. Hence, biological characteristics, such as fingerprints and irises [114], have been widely used as device passwords for further protecting the privacy information of users. This technique is called biometric verification.…”

Section: Applicationsmentioning

confidence: 99%

A Survey of Deep Facial Attribute Analysis

et al. 2020

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Facial attribute analysis has received considerable attention when deep learning techniques made remarkable breakthroughs in this field over the past few years. Deep learning based facial attribute analysis consists of two basic sub-issues: facial attribute estimation (FAE), which recognizes whether facial attributes are present in given images, and facial attribute manipulation (FAM), which synthesizes or removes desired facial attributes. In this paper, we provide a comprehensive survey of deep facial attribute analysis from the perspectives of both estimation and manipulation.First, we summarize a general pipeline that deep facial attribute analysis follows, which comprises two stages: data preprocessing and model construction. Additionally, we introduce the underlying theories of this twostage pipeline for both FAE and FAM. Second, the datasets and performance metrics commonly used in facial attribute analysis are presented. Third, we create a taxonomy of state-of-the-art methods and review deep FAE and FAM algorithms in detail. Furthermore, several additional facial attribute related issues are introduced, as well as relevant real-world applications. Finally, we discuss possible challenges and promising future research directions.

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Retinal Vascular Characteristics

Semerád

Drahanský

2019

Handbook of Vascular Biometrics

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This chapter begins with a description of eye anatomy followed by the anatomy of retinas as well as the acquisition methods for obtaining retinal images. Our own device for capturing the vascular pattern of the retina is introduced in the following text. This chapter presents our aim to estimate the information present in human retina images. The next section describes the search for diseases found in retinal images, and the last section is devoted to our method for generating synthetic retinal images.

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Iris Recognition After Death

Cited by 44 publications

References 17 publications

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

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

A Survey of Deep Facial Attribute Analysis

Retinal Vascular Characteristics

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