Transcending conventional biometry frontiers: Diffusive Dynamics PPG Biometry

In the first half of the 20th century, a first pulse oximeter was available to measure blood flow changes in the peripheral vascular net. However, it was not until recent times the PhotoPlethysmoGraphic (PPG) signal used to monitor many physiological parameters in clinical environments. Over the last decade, its use has extended to the area of biometrics, with different methods that allow the extraction of characteristic features of each individual from the PPG signal morphology, highly varying with time and the physical states of the subject. In this paper, we present a novel PPG-based biometric authentication system based on convolutional neural networks. Contrary to previous approaches, our method extracts the PPG signal's biometric characteristics from its diffusive dynamics, characterized by geometric patterns image in the (p, q)-planes specific to the 0-1 test. The diffusive dynamics of the PPG signal are strongly dependent on the vascular bed's biostructure, which is unique to each individual, and highly stable over time and other psychosomatic conditions. Besides its robustness, our biometric method is anti-spoofing, given the convoluted nature of the blood network. Our biometric authentication system reaches very low Equal Error Rates (ERRs) with a single attempt, making it possible, by the very nature of the envisaged solution, to implement it in miniature components easily integrated into wearable biometric systems.Comment: 18 pages, 6 figures, 4 table

Biometrics in ABC: counter-spoofing research

Automated border control (ABC) is concerned with fast and secure processing for intelligence-led identification. The FastPass project aims to build a harmonised, modular reference system for future European ABC. When biometrics is taken on board as identity, spoofing attacks become a concern. This paper presents current research in algorithm development for counter-spoofing attacks in biometrics. Focussing on three biometric traits, face, fingerprint, and iris, it examines possible types of spoofing attacks, and reviews existing algorithms reported in relevant academic papers in the area of countering measures to biometric spoofing attacks. It indicates that the new developing trend is fusion of multiple biometrics against spoofing attacks

Comprehensive Survey: Biometric User Authentication Application, Evaluation, and Discussion

This paper conducts an extensive review of biometric user authentication literature, addressing three primary research questions: (1) commonly used biometric traits and their suitability for specific applications, (2) performance factors such as security, convenience, and robustness, and potential countermeasures against cyberattacks, and (3) factors affecting biometric system accuracy and po-tential improvements. Our analysis delves into physiological and behavioral traits, exploring their pros and cons. We discuss factors influencing biometric system effectiveness and highlight areas for enhancement. Our study differs from previous surveys by extensively examining biometric traits, exploring various application domains, and analyzing measures to mitigate cyberattacks. This paper aims to inform researchers and practitioners about the biometric authentication landscape and guide future advancements

Automatic method for detection of characteristic areas in thermal face images

The use of thermal images of a selected area of the head in screening systems, which perform fast and accurate analysis of the temperature distribution of individual areas, requires the use of profiled image analysis methods. There exist methods for automated face analysis which are used at airports or train stations and are designed to detect people with fever. However, they do not enable automatic separation of specific areas of the face. This paper presents an algorithm for image analysis which enables localization of characteristic areas of the face in thermograms. The algorithm is resistant to subjects’ variability and also to changes in the position and orientation of the head. In addition, an attempt was made to eliminate the impact of background and interference caused by hair and hairline. The algorithm automatically adjusts its operation parameters to suit the prevailing room conditions. Compared to previous studies (Marzec et al., J Med Inform Tech 16:151–159, 2010), the set of thermal images was expanded by 34 images. As a result, the research material was a total of 125 patients’ thermograms performed in the Department of Pediatrics and Child and Adolescent Neurology in Katowice, Poland. The images were taken interchangeably with several thermal cameras: AGEMA 590 PAL (sensitivity of 0.1 °C), ThermaCam S65 (sensitivity of 0.08 °C), A310 (sensitivity of 0.05 °C), T335 (sensitivity of 0.05 °C) with a 320×240 pixel optical resolution of detectors, maintaining the principles related to taking thermal images for medical thermography. In comparison to (Marzec et al., J Med Inform Tech 16:151–159, 2010), the approach presented there has been extended and modified. Based on the comparison with other methods presented in the literature, it was demonstrated that this method is more complex as it enables to determine the approximate areas of selected parts of the face including anthropometry. As a result of this comparison, better results were obtained in terms of localization accuracy of the center of the eye sockets and nostrils, giving an accuracy of 87 % for the eyes and 93 % for the nostrils

A Survey of Wearable Biometric Recognition Systems

The growing popularity of wearable devices is leading to new ways to interact with the environment, with other smart devices, and with other people. Wearables equipped with an array of sensors are able to capture the owner’s physiological and behavioural traits, thus are well suited for biometric authentication to control other devices or access digital services. However, wearable biometrics have substantial differences from traditional biometrics for computer systems, such as fingerprints, eye features, or voice. In this article, we discuss these differences and analyse how researchers are approaching the wearable biometrics field. We review and provide a categorization of wearable sensors useful for capturing biometric signals. We analyse the computational cost of the different signal processing techniques, an important practical factor in constrained devices such as wearables. Finally, we review and classify the most recent proposals in the field of wearable biometrics in terms of the structure of the biometric system proposed, their experimental setup, and their results. We also present a critique of experimental issues such as evaluation and feasibility aspects, and offer some final thoughts on research directions that need attention in future work

Recent Application in Biometrics

In the recent years, a number of recognition and authentication systems based on biometric measurements have been proposed. Algorithms and sensors have been developed to acquire and process many different biometric traits. Moreover, the biometric technology is being used in novel ways, with potential commercial and practical implications to our daily activities. The key objective of the book is to provide a collection of comprehensive references on some recent theoretical development as well as novel applications in biometrics. The topics covered in this book reflect well both aspects of development. They include biometric sample quality, privacy preserving and cancellable biometrics, contactless biometrics, novel and unconventional biometrics, and the technical challenges in implementing the technology in portable devices. The book consists of 15 chapters. It is divided into four sections, namely, biometric applications on mobile platforms, cancelable biometrics, biometric encryption, and other applications. The book was reviewed by editors Dr. Jucheng Yang and Dr. Norman Poh. We deeply appreciate the efforts of our guest editors: Dr. Girija Chetty, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park and Dr. Sook Yoon, as well as a number of anonymous reviewers

Deep fingerprint classification network

Fingerprint is one of the most well-known biometrics that has been used for personal recognition. However, faked fingerprints have become the major enemy where they threat the security of this biometric. This paper proposes an efficient deep fingerprint classification network (DFCN) model to achieve accurate performances of classifying between real and fake fingerprints. This model has extensively evaluated or examined parameters. Total of 512 images from the ATVS-FFp_DB dataset are employed. The proposed DFCN achieved high classification performance of 99.22%, where fingerprint images are successfully classified into their two categories. Moreover, comparisons with state-of-art approaches are provided

A survey of wearable biometric recognition systems

The growing popularity of wearable devices is leading to new ways to interact with the environment, with other smart devices, and with other people. Wearables equipped with an array of sensors are able to capture the owner's physiological and behavioural traits, thus are well suited for biometric authentication to control other devices or access digital services. However, wearable biometrics have substantial differences from traditional biometrics for computer systems, such as fingerprints, eye features, or voice. In this article, we discuss these differences and analyse how researchers are approaching the wearable biometrics field. We review and provide a categorization of wearable sensors useful for capturing biometric signals. We analyse the computational cost of the different signal processing techniques, an important practical factor in constrained devices such as wearables. Finally, we review and classify the most recent proposals in the field of wearable biometrics in terms of the structure of the biometric system proposed, their experimental setup, and their results. We also present a critique of experimental issues such as evaluation and feasibility aspects, and offer some final thoughts on research directions that need attention in future work.This work was partially supported by the MINECO grant TIN2013-46469-R (SPINY) and the CAM Grant S2013/ICE-3095 (CIBERDINE