Andrey Kuehlkamp

Rasel Ahmed Bhuiyan, Parisa Farmanifard, Renu Sharma et al.

Post-mortem iris recognition brings both hope to the forensic community (a short-term but accurate and fast means of verifying identity) as well as concerns to society (its potential illicit use in post-mortem impersonation). These hopes and concerns have grown along with the volume of research in post-mortem iris recognition. Barriers to further progress in post-mortem iris recognition include the difficult nature of data collection, and the resulting small number of approaches designed specifically for comparing iris images of deceased subjects. This paper makes several unique contributions to mitigate these barriers. First, we have collected and we offer a new dataset of NIR (compliant with ISO/IEC 19794-6 where possible) and visible-light iris images collected after demise from 259 subjects, with the largest PMI (post-mortem interval) being 1,674 hours. For one subject, the data has been collected before and after death, the first such case ever published. Second, the collected dataset was combined with publicly-available post-mortem samples to assess the current state of the art in automatic forensic iris recognition with five iris recognition methods and data originating from 338 deceased subjects. These experiments include analyses of how selected demographic factors influence recognition performance. Thirdly, this study implements a model for detecting post-mortem iris images, which can be considered as presentation attacks. Finally, we offer an open-source forensic tool integrating three post-mortem iris recognition methods with explainability elements added to make the comparison process more human-interpretable.

Aidan Boyd, Daniel Moreira, Andrey Kuehlkamp et al.

Forensic iris recognition, as opposed to live iris recognition, is an emerging research area that leverages the discriminative power of iris biometrics to aid human examiners in their efforts to identify deceased persons. As a machine learning-based technique in a predominantly human-controlled task, forensic recognition serves as "back-up" to human expertise in the task of post-mortem identification. As such, the machine learning model must be (a) interpretable, and (b) post-mortem-specific, to account for changes in decaying eye tissue. In this work, we propose a method that satisfies both requirements, and that approaches the creation of a post-mortem-specific feature extractor in a novel way employing human perception. We first train a deep learning-based feature detector on post-mortem iris images, using annotations of image regions highlighted by humans as salient for their decision making. In effect, the method learns interpretable features directly from humans, rather than purely data-driven features. Second, regional iris codes (again, with human-driven filtering kernels) are used to pair detected iris patches, which are translated into pairwise, patch-based comparison scores. In this way, our method presents human examiners with human-understandable visual cues in order to justify the identification decision and corresponding confidence score. When tested on a dataset of post-mortem iris images collected from 259 deceased subjects, the proposed method places among the three best iris matchers, demonstrating better results than the commercial (non-human-interpretable) VeriEye approach. We propose a unique post-mortem iris recognition method trained with human saliency to give fully-interpretable comparison outcomes for use in the context of forensic examination, achieving state-of-the-art recognition performance.

Andrey Kuehlkamp, Aidan Boyd, Adam Czajka et al.

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.

Evan Brinckman, Andrey Kuehlkamp, Jarek Nabrzyski et al.

As the public Ethereum network surpasses half a billion transactions and enterprise Blockchain systems becoming highly capable of meeting the demands of global deployments, production Blockchain applications are fast becoming commonplace across a diverse range of business and scientific verticals. In this paper, we reflect on work we have been conducting recently surrounding the ingestion, retrieval and analysis of Blockchain data. We describe the scaling and semantic challenges when extracting Blockchain data in a way that preserves the original metadata of each transaction by cross referencing the Smart Contract interface with the on-chain data. We then discuss a scientific use case in the area of Scientific workflows by describing how we can harvest data from tasks and dependencies in a generic way. We then discuss how crawled public blockchain data can be analyzed using two unsupervised machine learning algorithms, which are designed to identify outlier accounts or smart contracts in the system. We compare and contrast the two machine learning methods and cross correlate with public Websites to illustrate the effectiveness such approaches.

Andrey Kuehlkamp, Kevin Bowyer

Predicting gender from iris images has been reported by several researchers as an application of machine learning in biometrics. Recent works on this topic have suggested that the preponderance of the gender cues is located in the periocular region rather than in the iris texture itself. This paper focuses on teasing out whether the information for gender prediction is in the texture of the iris stroma, the periocular region, or both. We present a larger dataset for gender from iris, and evaluate gender prediction accuracy using linear SVM and CNN, comparing hand-crafted and deep features. We use probabilistic occlusion masking to gain insight on the problem. Results suggest the discriminative power of the iris texture for gender is weaker than previously thought, and that the gender-related information is primarily in the periocular region.

Andrey Kuehlkamp, Kevin Bowyer

Iris recognition is used in many applications around the world, with enrollment sizes as large as over one billion persons in India's Aadhaar program. Large enrollment sizes can require special optimizations in order to achieve fast database searches. One such optimization that has been used in some operational scenarios is 1:First search. In this approach, instead of scanning the entire database, the search is terminated when the first sufficiently good match is found. This saves time, but ignores potentially better matches that may exist in the unexamined portion of the enrollments. At least one prominent and successful border-crossing program used this approach for nearly a decade, in order to allow users a fast "token-free" search. Our work investigates the search accuracy of 1:First and compares it to the traditional 1:N search. Several different scenarios are considered trying to emulate real environments as best as possible: a range of enrollment sizes, closed- and open-set configurations, two iris matchers, and different permutations of the galleries. Results confirm the expected accuracy degradation using 1:First search, and also allow us to identify acceptable working parameters where significant search time reduction is achieved, while maintaining accuracy similar to 1:N search.

Andrey Kuehlkamp, Benedict Becker, Kevin Bowyer

Predicting a person's gender based on the iris texture has been explored by several researchers. This paper considers several dimensions of experimental work on this problem, including person-disjoint train and test, and the effect of cosmetics on eyelash occlusion and imperfect segmentation. We also consider the use of multi-layer perceptron and convolutional neural networks as classifiers, comparing the use of data-driven and hand-crafted features. Our results suggest that the gender-from-iris problem is more difficult than has so far been appreciated. Estimating accuracy using a mean of N person-disjoint train and test partitions, and considering the effect of makeup - a combination of experimental conditions not present in any previous work - we find a much weaker ability to predict gender-from-iris texture than has been suggested in previous work.