Aug 2026· Medicine, Science and the Law· pp.
258024261477593
· 0 citations· 15 references
Medicine
TL;DR
The reliability and consistency of facial recognition technology is critically examined and case studies highlighting instances of wrongful detentions emphasize the urgent need for robust oversight, transparency and safeguards to ensure fairness, preventing misuse and upholding the dignity and privacy of individuals in the growing era of artificial intelligence.
Abstract
Facial recognition technology is an artificial intelligence-based biometric technology that stands at the forefront of global forensic investigations as a leading and widely adopted biometric modality. In comparison to alternative parameters such as voice, fingerprint, iris, retina, eye scan, gait, ear, and hand geometry; facial recognition emerges as the most popular and effective tool for personal identification and verification. Beyond its role in research, access control, user authentication, and border security, the technology plays a dynamic role in law enforcement and surveillance. Despite its extensive applications, the technology sparks privacy and ethical debates. Contemporary concerns revolve around its potential to implicate innocent individuals, raising issues of civil liberties, human rights, and privacy infringement. In light of these considerations, this article critically examines the reliability and consistency of facial recognition technology and incorporates case studies highlighting instances of wrongful detentions. It also explores the principles and functioning of facial recognition technology. The face recognition outcomes rely heavily on features that are extracted to reflect the face pattern and classification techniques used to distinguish between faces. However, there are a few limitations associated with the facial recognition technology. This is prone to errors in detecting some facial features and skin tones, which raises potential risks and consequences for innocent persons and communities. Therefore, these mistaken identity cases emphasize the urgent need for robust oversight, transparency and safeguards to ensure fairness, preventing misuse and upholding the dignity and privacy of individuals in the growing era of artificial intelligence. The present communication concludes with actionable recommendations to address the ethical and privacy challenges associated with facial recognition in the forensic context.
This article explores the global divide in regulating facial recognition technology for law enforcement, focusing on tensions between the European Union’s exceptions and the right to non-discrimination. Moving beyond privacy-based critiques, it grounds the analysis in equal treatment guarantees. Using Brazil as a case study, the article argues against reproducing the EU AI Act’s exceptions permitting real-time remote biometric identification by law enforcement. It demonstrates that even technically accurate FRT systems, when deployed within contexts of racialized policing and structural inequality, inevitably generate discriminatory outcomes. By highlighting the role of scale and limited contestability of algorithmic decisions, the article shows how real-time biometric identification produces a qualitatively distinct form of discriminatory harm. It concludes that Brazil, and countries facing similar structural disparities, should adopt an outright ban on real-time remote biometric identification for law enforcement, recognising that certain technological practices are inherently incompatible with constitutional guarantees of equality and non-discrimination.
Ana Maria Corrêa· Business and Human Rights Jo...· 0 citations
Attacks on general computer vision algorithms are often relegated to the digital domain, with the optimization performed purely in the digital world and then translated to physical mediums for implementation. In the field of biometrics, including facial recognition, physical presentation attacks targeting biometric sensors are dominant and present significant opportunity and risk. This paper highlights a critical vulnerability in the physical-to-digital pipeline of biometric sensors and provides a standardized approach for testing facial recognition system robustness against hardware attacks, going beyond and potentially complementing presentation attacks (as defined in ISO/IEC 30107 standard series). Specifically, in this work we (a) demonstrate that intentional electromagnetic interference is possible to be conducted with commonly accessible radio frequency (RF) equipment, (b) assess the robustness of state-of-the-art face recognition methods against RF-based attacks, and (c) provide a dataset composed of face images captured with and without electromagnetic interference to serve as a new benchmark for testing modern face matchers against RF-sourced interference.
Facial recognition technology (FRT) has expanded rapidly across public and semi-public spaces in India, including airports, railway stations, police surveillance systems, gated communities, and commercial establishments. Drawing on available survey data, civil society reports, legal analysis, and secondary literature, this paper offers a first systematic examination of Indian users’ perceptions of FRT deployed in physical environments. Findings indicate a complex, context-dependent picture: substantial public support exists for government and police use of FRT for security and crime control, yet significant concerns persist regarding privacy, accuracy biases (especially affecting women and certain communities), lack of consent and transparency, and the absence of a dedicated regulatory framework. Perceptions are shaped by trust in institutions, perceived utility, demographic factors, and lived experiences of surveillance. The paper highlights tensions between security benefits and rights-based risks, and outlines implications for policy, design, and future empirical research.
D. P. Kumar, Dr. K. Shobha Rani· International Journal of Cre...· 0 citations
Human Faces as a biometric feature are applied in multiple applications where both public and private organizations have preserved facial images for membership cards, passports, or personal identifications. They have been used as referential databases in investigations and match facial images of victims, witnesses, or offenders. Moreover, the widespread usages of smartphones and digital cameras have made it simple to share created facial images using social networks. Thus, Human Facial Recognitions (HFRs) have been an exciting and quickly expanding field of study. Real time applications include HFRs for identifications, access controls, forensics, and human-computer interactions. Though studies have been proposed for HFRs, there are areas that are wanting in implementations. Hence, this work attempts to fill up these gaps in HFRs with its suggested AI Based Recognitions of Faces (AIBRF). The schema uses Deep Learning (DL) techniques for identifying faces. The schema is trained using Yale Face dataset and achieves 99% accuracy in HRFs
Facial recognition in schools places two legitimate commitments in tension: protecting the school community and preserving the rights of children and adolescents whose faces may be converted into biometric data. This documentary study examines when facial recognition can be justified as a security resource and when its use begins to exceed the purpose that legitimized its adoption. The research is qualitative and uses Dialogical Discourse Analysis to interpret legislation, technical notes, educational policy documents and scientific literature on technology, school violence, personal data protection and facial recognition. Particular attention is given to the 2026 Brazilian National Data Protection Authority technical note concerning biometric attendance control in Paraná public schools. The analysis indicates that the ethical and educational significance of facial recognition changes according to purpose: identification and access control do not produce the same informational intervention as attendance registration, tracking of circulation or behavioral monitoring. The paper proposes the concept of a Biometric Intensification Staircase to represent this progression. It argues that security and privacy should not be treated as mutually exclusive values; however, the more technological identification advances from punctual protection to continuous observation, the stronger the requirements of necessity, proportionality, transparency, human review and data governance must become.
Ângela Maria dos Santos Rufino· Revista de Estudos Interdisc...· 0 citations
Biometric authentication systems based on a single modality remain vulnerable to spoofing, acquisition noise, and intra-class variation, limiting their reliability in high-security access-control applications. This study presents an artificial neural network-based multimodal framework that combines facial recognition and fingerprint identification to improve authentication accuracy, robustness, and presentation-attack resistance. Facial features are extracted using a convolutional neural network, while fingerprint texture and minutiae representations are obtained using Gabor filters and a denoising autoencoder. Feature-level and score-level information is integrated through a multilayer-perceptron meta-learner, followed by an adaptive decision module incorporating modality-specific liveness assessment. The framework was evaluated using the Labeled Faces in the Wild dataset, FVC2006, and the custom Bayelsa Multimodal Biometric Dataset comprising 320 subjects. On the reported BMBD test split, the system achieved 99.14% verification accuracy, a false acceptance rate of 0.12%, a false rejection rate of 0.34%, and an equal error rate of 0.19%. The reported performance exceeded the best unimodal baseline by 6.8 percentage points and the strongest compared multimodal method by 1.93 percentage points. End-to-end inference required 143 ms on the stated embedded platform. The ablation results indicated that learned fusion, metric-learning losses, and liveness detection each contributed to performance. These findings support the feasibility of the proposed framework under the reported experimental conditions, while broader independent evaluation remains necessary.
Eric Omianwele, Daniel Ekpah· Asian journal of current res...· 0 citations
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