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

Finger Vein Recognition with Hybrid Deep Learning Approach

Finger vein biometrics is an identification technique based on the vein patterns in fingers, and it has the benefit of being difficult to counterfeit. Due to its high level of security, durability, and performance history, finger vein recognition captures our attention as one of the most significant authentication methods available today. Using a mixed deep learning approach, we investigate the challenge of identifying the finger vein sensor model. Thus far, we use Traditional LSTM architectures for this biometric modality. This work also suggests a brand-new hybrid architecture that shines due to its compactness and a merging with the LSMT layer to be taught. In the experiment, original samples as well as the region of interest data from eight freely available FV-USM datasets are employed. The standard LSTM-based strategy is preferable and produced better outcomes, as seen by the comparison with the earlier approaches. Moreover, the results show that the hybrid CNN and LSTM networks may be used to improve vein detection performance

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

Fingervein Verification using Convolutional Multi-Head Attention Network

Biometric verification systems are deployed in various security-based access-control applications that require user-friendly and reliable person verification. Among the different biometric characteristics, fingervein biometrics have been extensively studied owing to their reliable verification performance. Furthermore, fingervein patterns reside inside the skin and are not visible outside; therefore, they possess inherent resistance to presentation attacks and degradation due to external factors. In this paper, we introduce a novel fingervein verification technique using a convolutional multihead attention network called VeinAtnNet. The proposed VeinAtnNet is designed to achieve light weight with a smaller number of learnable parameters while extracting discriminant information from both normal and enhanced fingervein images. The proposed VeinAtnNet was trained on the newly constructed fingervein dataset with 300 unique fingervein patterns that were captured in multiple sessions to obtain 92 samples per unique fingervein. Extensive experiments were performed on the newly collected dataset FV-300 and the publicly available FV-USM and FV-PolyU fingervein dataset. The performance of the proposed method was compared with five state-of-the-art fingervein verification systems, indicating the efficacy of the proposed VeinAtnNet.Comment: Accepted in IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 202

Pattern mining approaches used in sensor-based biometric recognition: a review

Sensing technologies place significant interest in the use of biometrics for the recognition and assessment of individuals. Pattern mining techniques have established a critical step in the progress of sensor-based biometric systems that are capable of perceiving, recognizing and computing sensor data, being a technology that searches for the high-level information about pattern recognition from low-level sensor readings in order to construct an artificial substitute for human recognition. The design of a successful sensor-based biometric recognition system needs to pay attention to the different issues involved in processing variable data being - acquisition of biometric data from a sensor, data pre-processing, feature extraction, recognition and/or classification, clustering and validation. A significant number of approaches from image processing, pattern identification and machine learning have been used to process sensor data. This paper aims to deliver a state-of-the-art summary and present strategies for utilizing the broadly utilized pattern mining methods in order to identify the challenges as well as future research directions of sensor-based biometric systems

IEEE Access Special Section Editorial: Biologically Inspired Image Processing Challenges and Future Directions

Human kind is exposed to large amounts of data. According to statistics, more than 80% of information received by humans comes from the visual system. Therefore, image information processing is not only an important research topic but also a challenging task. The unique information processing mechanism of the human visual system provides it with fast, accurate, and efficient image processing capabilities. At present, many advanced image analysis and processing techniques have been widely used in image communication, geographic information systems, medical image analysis, and virtual reality. However, there is still a large gap between these technologies and the human visual system. Therefore, building an image system research mechanism based on the biological vision system is an attractive but difficult target. Although it is a challenge, it can also be considered as an opportunity which utilizes biologically inspired ideas. Meanwhile, through the integration of neural biology, biological perception mechanisms, and computer science and mathematical science, related research can bridge biological vision and computer vision. Finally, the biologically inspired image analysis and processing system is expected to be built on the basis of further consideration of the learning mechanism of the human brain

An overview of touchless 2D fingerprint recognition

Touchless fingerprint recognition represents a rapidly growing field of research which has been studied for more than a decade. Through a touchless acquisition process, many issues of touch-based systems are circumvented, e.g., the presence of latent fingerprints or distortions caused by pressing fingers on a sensor surface. However, touchless fingerprint recognition systems reveal new challenges. In particular, a reliable detection and focusing of a presented finger as well as an appropriate preprocessing of the acquired finger image represent the most crucial tasks. Also, further issues, e.g., interoperability between touchless and touch-based fingerprints or presentation attack detection, are currently investigated by different research groups. Many works have been proposed so far to put touchless fingerprint recognition into practice. Published approaches range from self identification scenarios with commodity devices, e.g., smartphones, to high performance on-the-move deployments paving the way for new fingerprint recognition application scenarios.This work summarizes the state-of-the-art in the field of touchless 2D fingerprint recognition at each stage of the recognition process. Additionally, technical considerations and trade-offs of the presented methods are discussed along with open issues and challenges. An overview of available research resources completes the work

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