The adoption of large-scale iris recognition systems around the world has brought to light the importance of detecting presentation attack images (textured contact lenses and printouts). This paper presents a new approach in iris presentation attack detection (PAD) by exploring combinations of convolutional neural networks (CNNs) and transformed input spaces through binarized statistical image features (BSIFs). Our method combines lightweight CNNs to classify multiple BSIF views of the input image. Following explorations on complementary input spaces leading to more discriminative features to detect presentation attacks, we also propose an algorithm to select the best (and most discriminative) predictors for the task at hand. An ensemble of predictors makes use of their expected individual performances to aggregate their results into a final prediction. Results show that this technique improves on the current state of the art in iris PAD, outperforming the winner of LivDet-Iris 2017 competition both for intra- and cross-dataset scenarios, and illustrating the very difficult nature of the cross-dataset scenario.

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.

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.

Recent advances in machine learning (ML) across various fields have been remarkable. Integration of ML in healthcare has marked a new era, enabling significant improvements such as enhanced diagnostic accuracy, treatment plans, and better patient outcomes. ML algorithms have proved effective in financial industries in detecting fraud by analyzing transaction patterns. In the retail industry, ML improves customer experience through personalized recommendations. Additionally, ML is critical for improving natural language processing, robotics, and other automation processes. However, the rapid evolution of ML makes it necessary to ensure responsible data use, highlighting the importance of privacy of user data, fairness, and transparency. It is crucial to balance the breakthroughs made by ML with ethical considerations. Implementation of FAIR principles in ML is vital under these circumstances. These principles guide ethical and responsible use of not only data but also ML models, thereby enhancing trust and aligning with societal values. This article presents a systematic literature review of the current state of application of FAIR principles in the field of ML. We aim to understand the existing systems that are used in the ML domain for management of data and model lifecycle while adhering to FAIR principles. We have also presented an overview of various proposals and ongoing initiatives that aim to follow the FAIR movement to enable long-term preservation of ML models and data. This enables future advancement of domains by leveraging already existing models and datasets. Additionally, we discuss our ongoing and future work, which explores how technologies such as blockchain and self-sovereign identity can be used to verify data veracity, transparently create and manage data lineage, provide access control mechanisms to ensure responsible data usage while respecting privacy of users and confidentiality of their data used in the design, development, training and testing of ML models.

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.