Because biometric data is sensitive, centralized training poses a privacy risk, even though biometric recognition is essential for contemporary applications. Federated learning (FL), which permits decentralized training, provides a privacy-preserving substitute. Conventional FL, however, has trouble with interpretability and heterogeneous data (non-IID). In order to handle non-IID biometric data, this framework adds an attention mechanism at the central server that weights local model updates according to their significance. Differential privacy and secure update protocols safeguard data while preserving accuracy. The A3-FL framework is evaluated in this study using FVC2004 fingerprint data, with each client's features extracted using a Siamese Convolutional Neural Network (Siamese-CNN). By dynamically modifying client contributions, the attention mechanism increases the accuracy of the global model.The accuracy, convergence speed, and robustness of the A3-FL framework are superior to those of standard FL (FedAvg) and static baselines, according to experimental evaluations using fingerprint data (FVC2004). The accuracy of the attention-based approach was 0.8413, while FedAvg, Local-only, and Centralized approaches were 0.8164, 0.7664, and 0.7997, respectively. Accuracy stayed high at 0.8330 even with differential privacy. A scalable and privacy-sensitive biometric system for secure and effective recognition in dispersed environments is presented in this work.

Photorealistic talking-head avatars are becoming increasingly common in virtual meetings, gaming, and social platforms. These avatars allow for more immersive communication, but they also introduce serious security risks. One emerging threat is impersonation: an attacker can steal a user's avatar, preserving his appearance and voice, making it nearly impossible to detect its fraudulent usage by sight or sound alone. In this paper, we explore the challenge of biometric verification in such avatar-mediated scenarios. Our main question is whether an individual's facial motion patterns can serve as reliable behavioral biometrics to verify their identity when the avatar's visual appearance is a facsimile of its owner. To answer this question, we introduce a new dataset of realistic avatar videos created using a state-of-the-art one-shot avatar generation model, GAGAvatar, with genuine and impostor avatar videos. We also propose a lightweight, explainable spatio-temporal Graph Convolutional Network architecture with temporal attention pooling, that uses only facial landmarks to model dynamic facial gestures. Experimental results demonstrate that facial motion cues enable meaningful identity verification with AUC values approaching 80%. The proposed benchmark and biometric system are available for the research community in order to bring attention to the urgent need for more advanced behavioral biometric defenses in avatar-based communication systems.

Fingerprint mosaicking, which is the process of combining multiple fingerprint images into a single master fingerprint, is an essential process in modern biometric systems. However, it is prone to errors that can significantly degrade fingerprint image quality. This paper proposes a novel deep learning-based approach to detect and score mosaicking artifacts in fingerprint images. Our method leverages a self-supervised learning framework to train a model on large-scale unlabeled fingerprint data, eliminating the need for manual artifact annotation. The proposed model effectively identifies mosaicking errors, achieving high accuracy on various fingerprint modalities, including contactless, rolled, and pressed fingerprints and furthermore proves to be robust to different data sources. Additionally, we introduce a novel mosaicking artifact score to quantify the severity of errors, enabling automated evaluation of fingerprint images. By addressing the challenges of mosaicking artifact detection, our work contributes to improving the accuracy and reliability of fingerprint-based biometric systems.

Biometrics involves using unique human traits, both physical and behavioral, for the digital identification of individuals to provide access to systems, devices, or information. Within the field of computer science, it acts as a method for identifying and verifying individuals and controlling access. While the conventional method for personal authentication involves passwords, the vulnerability arises when passwords are compromised, allowing unauthorized access to sensitive actions. Biometric authentication presents a viable answer to this problem and is the most secure and user-friendly authentication method. Today, fingerprints, iris and retina patterns, facial recognition, hand shapes, palm prints, and voice recognition are frequently used forms of biometrics. Despite the diverse nature of these biometric identifiers, the core objective remains consistent ensuring security, recognizing authorized users, and rejecting impostors. Hence, it is crucial to determine accurately whether the characteristics belong to the rightful person. For systems to be effective and widely accepted, the error rate in recognition and verification must approach zero. It is acknowledged that current biometric techniques, while advanced, are not infallible and require continuous improvement. A more refined classifier is deemed necessary to classify patterns accurately. Artificial Neural Networks, which simulate the human brain's operations, present themselves as a promising approach. The survey presented herein explores various biometric techniques based on neural networks, emphasizing the ongoing quest for enhanced accuracy and reliability. It concludes that The utilization of neural networks along with biometric features not only enhances accuracy but also contributes to overall better security.

This work, in a pioneering approach, attempts to build a biometric system that works purely based on the fluid mechanics governing exhaled breath. We test the hypothesis that the structure of turbulence in exhaled human breath can be exploited to build biometric algorithms. This work relies on the idea that the extrathoracic airway is unique for every individual, making the exhaled breath a biomarker. Methods including classical multi-dimensional hypothesis testing approach and machine learning models are employed in building user authentication algorithms, namely user confirmation and user identification. A user confirmation algorithm tries to verify whether a user is the person they claim to be. A user identification algorithm tries to identify a user's identity with no prior information available. A dataset of exhaled breath time series samples from 94 human subjects was used to evaluate the performance of these algorithms. The user confirmation algorithms performed exceedingly well for the given dataset with over $97\%$ true confirmation rate. The machine learning based algorithm achieved a good true confirmation rate, reiterating our understanding of why machine learning based algorithms typically outperform classical hypothesis test based algorithms. The user identification algorithm performs reasonably well with the provided dataset with over $50\%$ of the users identified as being within two possible suspects. We show surprisingly unique turbulent signatures in the exhaled breath that have not been discovered before. In addition to discussions on a novel biometric system, we make arguments to utilise this idea as a tool to gain insights into the morphometric variation of extrathoracic airway across individuals. Such tools are expected to have future potential in the area of personalised medicines.

Biometry has proved its capability in terms of recognition accuracy. Now, it is widely used for automated border control with the biometric passport, to unlock a smartphone or a computer with a fingerprint or a face recognition algorithm. While identity verification is widely democratized, pure identification with no additional clues is still a work in progress. The identification difficulty depends on the population size, as the larger the group is, the larger the confusion risk. For collision prevention, biometric traits must be sufficiently distinguishable to scale to considerable groups, and algorithms should be able to capture their differences accurately. Most biometric works are purely experimental, and it is impossible to extrapolate the results to a smaller or a larger group. In this work, we propose a theoretical analysis of the distinguishability problem, which governs the error rates of biometric systems. We demonstrate simple relationships between the population size and the number of independent bits necessary to prevent collision in the presence of noise. This work provides the lowest lower bound for memory requirements. The results are very encouraging, as the biometry of the whole Earth population can fit in a regular disk, leaving some space for noise and redundancy.

The demographic disparity of biometric systems has led to serious concerns regarding their societal impact as well as applicability of such systems in private and public domains. A quantitative evaluation of demographic fairness is an important step towards understanding, assessment, and mitigation of demographic bias in biometric applications. While few, existing fairness measures are based on post-decision data (such as verification accuracy) of biometric systems, we discuss how pre-decision data (score distributions) provide useful insights towards demographic fairness. In this paper, we introduce multiple measures, based on the statistical characteristics of score distributions, for the evaluation of demographic fairness of a generic biometric verification system. We also propose different variants for each fairness measure depending on how the contribution from constituent demographic groups needs to be combined towards the final measure. In each case, the behavior of the measure has been illustrated numerically and graphically on synthetic data. The demographic imbalance in benchmarking datasets is often overlooked during fairness assessment. We provide a novel weighing strategy to reduce the effect of such imbalance through a non-linear function of sample sizes of demographic groups. The proposed measures are independent of the biometric modality, and thus, applicable across commonly used biometric modalities (e.g., face, fingerprint, etc.).

All of those fantastical possibilities promised by burgeoning brain-computer interface technology come with the unavoidable cost of needing its potentially hackable wetware to ride shotgun in your skull. Given how often our personal data is already mishandled online, do we really want to trust the Tech Bros of Silicon Valley with our most personal of biometrics, our brainwaves? In her new book, The Battle for Your Brain: Defending the Right to Think Freely in the Age of Neurotechnology, Robinson O. Everett Professor of Law at Duke University, Nita A. Farahany, examines the legal, ethical, and moral threats that tomorrow's neurotechnologies could pose. From The Battle for Your Brain: Defending the Right to Think Freely in the Age of Neurotechnology by Nita A. Farahany. Assume that Meta, Google, Microsoft, and other big tech companies soon have their way, and neural interface devices replace keyboards and mice.

The government of Morocco is looking for a contractor to install facial recognition systems in that nation's Rabat-Sale Airport. It reportedly would be the first such facility in the nation to have face biometrics. Officials want a One ID biometric system in a new terminal. A tender notification (103-22-A00) was published this week; it closes September 15. According to the Morocco World News, the National Airports Office has received a MAD363 million (approximately US$37 million) loan to upgrade Rabat-Sale.

Yet many of these systems are not accessible to people who experience different kinds of disability exclusion. Different personal characteristics may impact any or all of the physical (DNA, fingerprints, face or retina) and behavioral (gesture, gait, voice) characteristics listed in the RFI as examples of biometric signals. We define disability here in terms of the discriminatory and often systemic problems with available infrastructure's ability to meet the needs of all people [UN 2017, Oliver, 2013). Using this definition, "[biometrics] could either mitigate or amplify disability depending on how they are designed." (Guo, 2019). As Whittaker and colleauges (2019) state, this is not simply a matter of algorithmic accuracy: "...discrimination against people of color, women, and other historically marginalized groups has often been justified by representing these groups as disabled . Thus disability is entwined with, and serves to justify, practices of marginalization." It is critical that we look beyond inclusion to full and fully accommodated participation.