Evaluation of a Vein Biometric Recognition System on an Ordinary Smartphone

Nowadays, biometrics based on vein patterns as a trait is a promising technique. Vein patterns satisfy universality, distinctiveness, permanence, performance, and protection against circumvention. However, collectability and acceptability are not completely satisfied. These two properties are directly related to acquisition methods. The acquisition of vein images is usually based on the absorption of near-infrared (NIR) light by the hemoglobin inside the veins, which is higher than in the surrounding tissues. Typically, specific devices are designed to improve the quality of the vein images. However, such devices increase collectability costs and reduce acceptability. This paper focuses on using commercial smartphones with ordinary cameras as potential devices to improve collectability and acceptability. In particular, we use smartphone applications (apps), mainly employed for medical purposes, to acquire images with the smartphone camera and improve the contrast of superficial veins, as if using infrared LEDs. A recognition system has been developed that employs the free IRVeinViewer App to acquire images from wrists and dorsal hands and a feature extraction algorithm based on SIFT (scale-invariant feature transform) with adequate pre- and post-processing stages. The recognition performance has been evaluated with a database composed of 1000 vein images associated to five samples from 20 wrists and 20 dorsal hands, acquired at different times of day, from people of different ages and genders, under five different environmental conditions: day outdoor, indoor with natural light, indoor with natural light and dark homogeneous background, indoor with artificial light, and darkness. The variability of the images acquired in different sessions and under different ambient conditions has a large influence on the recognition rates, such that our results are similar to other systems from the literature that employ specific smartphones and additional light sources. Since reported quality assessment algorithms do not help to reject poorly acquired images, we have evaluated a solution at enrollment and matching that acquires several images subsequently, computes their similarity, and accepts only the samples whose similarity is greater than a threshold. This improves the recognition, and it is practical since our implemented system in Android works in real-time and the usability of the acquisition app is high.MCIN/AEI/ 10.13039/50110001103 Grant PDC2021-121589-I00Fondo Europeo de Desarrollo Regional (FEDER) and Consejería de Transformación Económica, Industria, Conocimiento y Universidades de la Junta de Andalucía Grant US-126514

Vein biometric recognition on a smartphone

Topic: Intelligent Biometric Systems for Secure Societies.Human recognition on smartphone devices for unlocking, online payment, and bank account verification is one of the significant uses of biometrics. The exponential development and integration of this technology have been established since the introduction in 2013 of the fingerprint mounted sensor in the Apple iPhone 5s by Apple Inc.© (Motorola© Atrix was previously launched in 2011). Nowadays, in the commercial world, the main biometric variants integrated into mobile devices are fingerprint, facial, iris, and voice. In 2019, LG© Electronics announced the first mobile exhibiting vascular biometric recognition, integrated using the palm vein modality: LG© G8 ThinQ (hand ID). In this work, in an attempt to become the become the first research-embedded approach to smartphone vein identification, a novel wrist vascular biometric recognition is designed, implemented, and tested on the Xiaomi© Pocophone F1 and the Xiaomi© Mi 8 devices. The near-infrared camera mounted for facial recognition on these devices accounts for the hardware employed. Two software algorithms, TGS-CVBR® and PIS-CVBR®, are designed and applied to a database generation and the identification task, respectively. The database, named UC3M-Contactless Version 2 (UC3M-CV2), consists of 2400 contactless infrared images from both wrists of 50 different subjects (25 females and 25 males, 100 individual wrists in total), collected in two separate sessions with different environmental light environmental light conditions. The vein biometric recognition, using PIS-CVBR®, is based on the SIFT®, SURF®, and ORB algorithms. The results, discussed according to the ISO/IEC 19795-1:2019 standard, are promising and pave the way for contactless real-time-processing wrist recognition on smartphone devices

Biometric Systems

Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study

Deep Learning for Vein Biometric Recognition on a Smartphone

The ongoing COVID-19 pandemic has pointed out, even more, the important need for hygiene contactless biometric recognition systems. Vein-based devices are great non-contact options although they have not been entirely well-integrated in daily life. In this work, in an attempt to contribute to the research and development of these devices, a contactless wrist vein recognition system with a real-life application is revealed. A Transfer Learning (TL) method, based on different Deep Convolutional Neural Networks architectures, for Vascular Biometric Recognition (VBR), has been designed and tested, for the first time in a research approach, on a smartphone. TL is a Deep Learning (DL) technique that could be divided into networks as feature extractor, i.e., using a pre-trained (different large-scale dataset) Convolutional Neural Network (CNN) to obtain unique features that then, are classified with a traditional Machine Learning algorithm, and fine-tuning, i.e., training a CNN that has been initialized with weights of a pre-trained (different large-scale dataset) CNN. In this study, a feature extractor base method has been employed. Several architecture networks have been tested on different wrist vein datasets: UC3M-CV1, UC3M-CV2, and PUT. The DL model has been integrated on the Xiaomi© Pocophone F1 and the Xiaomi© Mi 8 smartphones obtaining high biometric performance, up to 98% of accuracy and less than 0.4% of EER with a 50–50% train-test on UC3M-CV2, and fast identification/verification time, less than 300 milliseconds. The results infer, high DL performance and integration reachable in VBR without direct user-device contact, for real-life applications nowadays

PROTECT: pervasive and useR fOcused biomeTrics bordEr projeCT. A Case Study

PROTECT: Pervasive and useR fOcused biomeTrics bordEr projeCT is an EU project funded by the Horizon 2020 research and Innovation Programme. The main aim of PROTECT was to build an advanced biometric-based person identification system that works robustly across a range of border crossing types and that has strong user-centric features. This work presents the case study of the multibiometric verification system developed within PROTECT. The system has been developed to be suitable for different borders such as air, sea, and land borders. The system covers two use cases: the walk-through scenario, in which the traveller is on foot; the drive-through scenario, in which the traveller is in a vehicle. Each deployment includes a different set of biometric traits and this paper illustrates how to evaluate such multibiometric system in accordance with international standards and, in particular, how to overcome practical problems that may be encountered when dealing with multibiometric evaluation, such as different score distributions and missing scores

Handbook of Vascular Biometrics

This open access handbook provides the first comprehensive overview of biometrics exploiting the shape of human blood vessels for biometric recognition, i.e. vascular biometrics, including finger vein recognition, hand/palm vein recognition, retina recognition, and sclera recognition. After an introductory chapter summarizing the state of the art in and availability of commercial systems and open datasets/open source software, individual chapters focus on specific aspects of one of the biometric modalities, including questions of usability, security, and privacy. The book features contributions from both academia and major industrial manufacturers

Biometric Spoofing: A JRC Case Study in 3D Face Recognition

Based on newly available and affordable off-the-shelf 3D sensing, processing and printing technologies, the JRC has conducted a comprehensive study on the feasibility of spoofing 3D and 2.5D face recognition systems with low-cost self-manufactured models and presents in this report a systematic and rigorous evaluation of the real risk posed by such attacking approach which has been complemented by a test campaign. The work accomplished and presented in this report, covers theories, methodologies, state of the art techniques, evaluation databases and also aims at providing an outlook into the future of this extremely active field of research.JRC.G.6-Digital Citizen Securit

A STUDY OF VEIN RECOGNITION SYSTEM

The vein recognition is the most accurate and secure technology in the field of biometrics. We have seen that many criminal cases like risk of forgery or theft. All biometric traits have their own advantages and disadvantages. Like in long term, a person’s fingerprint may be damaged due to environment, aging or ethnicity; face recognition has medium accuracy; iris recognition is costly affair. To overcome all the problems, the solution is vein recognition. The most important point about vein recognition system is that it works on living persons only as the infrared radiation is absorbed by hemoglobin present in blood of living persons. The skin integrity does not affect the accuracy or readability of finger vein recognition. There are lots of benefits to include vein recognition in biometric traits in which some are, users are not required to get in physical contact with the device. This technology can also be used for medical purposes like children that are below the age of 12 have very smooth vein which sometime cannot be detected by doctors to inject injection which can be detected by NIR cameras. In this paper we have presented the various techniques of palm vein recognition that are applied in today’s scenario, Need and Scope of vein recognition system, its implementation challenges, its application, Merit and Demerit and finally conclusion