Biometric authentication is the recognition of human identity via unique anatomical features. The development of novel methods parallels widespread application by consumer devices, law enforcement, and access control. In particular, methods based on finger veins, as compared to face and fingerprints, obviate privacy concerns and degradation due to wear, age, and obscuration. However, they are two-dimensional (2D) and are fundamentally limited by conventional imaging and tissue-light scattering. In this work, for the first time, to the best of our knowledge, we demonstrate a method of three-dimensional (3D) finger vein biometric authentication based on photoacoustic tomography. Using a compact photoacoustic tomography setup and a novel recognition algorithm, the advantages of 3D are demonstrated via biometric authentication of index finger vessels with false acceptance, false rejection, and equal error rates <1.23, <9.27, and <0.13, respectively, when comparing one finger, a false acceptance >10× when comparing multiple fingers, and <0.7 when rotating fingers ±30.
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http://dx.doi.org/10.1364/AO.400550 | DOI Listing |
Sensors (Basel)
December 2024
Department of Biomedical Engineering, Lebanese International University, Beirut P.O. Box 146404, Lebanon.
The integration of liveness detection into biometric systems is crucial for countering spoofing attacks and enhancing security. This study investigates the efficacy of photoplethysmography (PPG) signals, which offer distinct advantages over traditional biometric techniques. PPG signals are non-invasive, inherently contain liveness information that is highly resistant to spoofing, and are cost-efficient, making them a superior alternative for biometric authentication.
View Article and Find Full Text PDFNat Commun
January 2025
Data Science Institute, Imperial College London, London, UK.
AI techniques are increasingly being used to identify individuals both offline and online. However, quantifying their effectiveness at scale and, by extension, the risks they pose remains a significant challenge. Here, we propose a two-parameter Bayesian model for exact matching techniques and derive an analytical expression for correctness (κ), the fraction of people accurately identified in a population.
View Article and Find Full Text PDFAnal Chim Acta
January 2025
State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products, School of Material Science and Chemical Engineering, Ningbo University, Ningbo, 315211, PR China. Electronic address:
Background: Foodborne pathogens, particularly Vibrio parahaemolyticus (VP) found in seafood, pose significant health risks, including abdominal pain, nausea, and even death. Rapid, accurate, and sensitive detection of these pathogens is crucial for food safety and public health. However, existing detection methods often require complex sample pretreatment, which limits their practical application.
View Article and Find Full Text PDFCleft Palate Craniofac J
January 2025
Division of Plastic and Reconstructive Surgery, Oregon Health & Science University, Portland, OR, USA.
Craniosynostosis is rarely diagnosed in utero. Prenatal diagnosis has the potential to improve patient outcomes and streamline care, however, and is becoming more feasible as technology improves. The objective of this study is to examine existing literature on prenatal diagnosis of nonsyndromic craniosynostosis.
View Article and Find Full Text PDFBiomed Eng Lett
January 2025
Department of Electronic Engineering, Hanyang University, Seoul, 04763 Republic of Korea.
Demand for user authentication in virtual reality (VR) applications is increasing such as in-app payments, password manager, and access to private data. Traditionally, hand controllers have been widely used for the user authentication in VR environment, with which the users can typewrite a password or draw a pre-registered pattern; however, the conventional approaches are generally inconvenient and time-consuming. In this study, we proposed a new user authentication method based on eye-writing patterns identified using electrooculogram (EOG) recorded from four locations around the eyes in contact with the face-pad of a VR headset.
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