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

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

Biometrics

Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book

Short Papers of the 8th Conference on Cloud Computing Conference, Big Data & Emerging Topics (JCC-BD&ET 2020)

Compilación de los short papers presentados en las 8vas Jornadas de Cloud Computing, Big Data & Emerging Topics (JCC-BD&ET2020), llevadas a cabo en modalidad virtual durante septiembre de 2020 y organizadas por el Instituto de Investigación en Informática LIDI (III-LIDI) y la Secretaría de Posgrado de la Facultad de Informática de la UNLP en colaboración con universidades de Argentina y del exterior.Facultad de Informátic

Signal processing and machine learning techniques for human verification based on finger textures

PhD ThesisIn recent years, Finger Textures (FTs) have attracted considerable attention as potential biometric characteristics. They can provide robust recognition performance as they have various human-speci c features, such as wrinkles and apparent lines distributed along the inner surface of all ngers. The main topic of this thesis is verifying people according to their unique FT patterns by exploiting signal processing and machine learning techniques. A Robust Finger Segmentation (RFS) method is rst proposed to isolate nger images from a hand area. It is able to detect the ngers as objects from a hand image. An e cient adaptive nger segmentation method is also suggested to address the problem of alignment variations in the hand image called the Adaptive and Robust Finger Segmentation (ARFS) method. A new Multi-scale Sobel Angles Local Binary Pattern (MSALBP) feature extraction method is proposed which combines the Sobel direction angles with the Multi-Scale Local Binary Pattern (MSLBP). Moreover, an enhanced method called the Enhanced Local Line Binary Pattern (ELLBP) is designed to e ciently analyse the FT patterns. As a result, a powerful human veri cation scheme based on nger Feature Level Fusion with a Probabilistic Neural Network (FLFPNN) is proposed. A multi-object fusion method, termed the Finger Contribution Fusion Neural Network (FCFNN), combines the contribution scores of the nger objects. The veri cation performances are examined in the case of missing FT areas. Consequently, to overcome nger regions which are poorly imaged a method is suggested to salvage missing FT elements by exploiting the information embedded within the trained Probabilistic Neural Network (PNN). Finally, a novel method to produce a Receiver Operating Characteristic (ROC) curve from a PNN is suggested. Furthermore, additional development to this method is applied to generate the ROC graph from the FCFNN. Three databases are employed for evaluation: The Hong Kong Polytechnic University Contact-free 3D/2D (PolyU3D2D), Indian Institute of Technology (IIT) Delhi and Spectral 460nm (S460) from the CASIA Multi-Spectral (CASIAMS) databases. Comparative simulation studies con rm the e ciency of the proposed methods for human veri cation. The main advantage of both segmentation approaches, the RFS and ARFS, is that they can collect all the FT features. The best results have been benchmarked for the ELLBP feature extraction with the FCFNN, where the best Equal Error Rate (EER) values for the three databases PolyU3D2D, IIT Delhi and CASIAMS (S460) have been achieved 0.11%, 1.35% and 0%, respectively. The proposed salvage approach for the missing feature elements has the capability to enhance the veri cation performance for the FLFPNN. Moreover, ROC graphs have been successively established from the PNN and FCFNN.the ministry of higher education and scientific research in Iraq (MOHESR); the Technical college of Mosul; the Iraqi Cultural Attach e; the active people in the MOHESR, who strongly supported Iraqi students

Analysis Of Data Stratification In A Multi-Sensor Fingerprint Dataset Using Match Score Statistics

Biometric data is an essential feature employed in testing the performance of any real time biometric recognition system prior to its usage. The variations introduced in the match performance critically determine the authenticity of the biometric data to be able to be used in an everyday scenario for the testing of biometric verification systems. This study in totality aims at understanding the impact of data stratification of a such a biometric test dataset on the match performance of each of its stratum. In order to achieve this goal, the fingerprint dataset of the West Virginia University\u27s 2012 BioCOP has been employed which is a part of the many multimodal biometric data collection projects that the University has accomplished. This test dataset has been initially segmented based on the scanners employed in the process of data acquisition to check for the variations in match performance with reference to the acquisition device. The secondary stage of data stratification included the creation of stratum based on the demographic features of the subjects in the dataset.;The main objectives this study aims to achieve are:;• Developing a framework to assess the match score distributions of each stratum..;• Assessing the match performance of demographic strata in comparison to the total dataset..;• Statistical match performance evaluation using match score statistics..;Following the generation of genuine and imposter match score distributions , Receiver Operating Characteristic Curves (ROC) were plotted to compare the match performance of each demographic stratum with respect to the total dataset. The divergence measures KLD and JSD have been calculated which signify the amount of variation between the match score distributions of each stratum. With the help of these procedures, the task of estimating the effect of data stratification on the match performance has been accomplished which serves as a measure of understanding the impact of this fingerprint dataset when used for biometric testing purposes

Vulnerability assessment in the use of biometrics in unsupervised environments

Mención Internacional en el título de doctorIn the last few decades, we have witnessed a large-scale deployment of biometric systems in different life applications replacing the traditional recognition methods such as passwords and tokens. We approached a time where we use biometric systems in our daily life. On a personal scale, the authentication to our electronic devices (smartphones, tablets, laptops, etc.) utilizes biometric characteristics to provide access permission. Moreover, we access our bank accounts, perform various types of payments and transactions using the biometric sensors integrated into our devices. On the other hand, different organizations, companies, and institutions use biometric-based solutions for access control. On the national scale, police authorities and border control measures use biometric recognition devices for individual identification and verification purposes. Therefore, biometric systems are relied upon to provide a secured recognition where only the genuine user can be recognized as being himself. Moreover, the biometric system should ensure that an individual cannot be identified as someone else. In the literature, there are a surprising number of experiments that show the possibility of stealing someone’s biometric characteristics and use it to create an artificial biometric trait that can be used by an attacker to claim the identity of the genuine user. There were also real cases of people who successfully fooled the biometric recognition system in airports and smartphones [1]–[3]. That urges the necessity to investigate the potential threats and propose countermeasures that ensure high levels of security and user convenience. Consequently, performing security evaluations is vital to identify: (1) the security flaws in biometric systems, (2) the possible threats that may target the defined flaws, and (3) measurements that describe the technical competence of the biometric system security. Identifying the system vulnerabilities leads to proposing adequate security solutions that assist in achieving higher integrity. This thesis aims to investigate the vulnerability of fingerprint modality to presentation attacks in unsupervised environments, then implement mechanisms to detect those attacks and avoid the misuse of the system. To achieve these objectives, the thesis is carried out in the following three phases. In the first phase, the generic biometric system scheme is studied by analyzing the vulnerable points with special attention to the vulnerability to presentation attacks. The study reviews the literature in presentation attack and the corresponding solutions, i.e. presentation attack detection mechanisms, for six biometric modalities: fingerprint, face, iris, vascular, handwritten signature, and voice. Moreover, it provides a new taxonomy for presentation attack detection mechanisms. The proposed taxonomy helps to comprehend the issue of presentation attacks and how the literature tried to address it. The taxonomy represents a starting point to initialize new investigations that propose novel presentation attack detection mechanisms. In the second phase, an evaluation methodology is developed from two sources: (1) the ISO/IEC 30107 standard, and (2) the Common Evaluation Methodology by the Common Criteria. The developed methodology characterizes two main aspects of the presentation attack detection mechanism: (1) the resistance of the mechanism to presentation attacks, and (2) the corresponding threat of the studied attack. The first part is conducted by showing the mechanism's technical capabilities and how it influences the security and ease-of-use of the biometric system. The second part is done by performing a vulnerability assessment considering all the factors that affect the attack potential. Finally, a data collection is carried out, including 7128 fingerprint videos of bona fide and attack presentation. The data is collected using two sensing technologies, two presentation scenarios, and considering seven attack species. The database is used to develop dynamic presentation attack detection mechanisms that exploit the fingerprint spatio-temporal features. In the final phase, a set of novel presentation attack detection mechanisms is developed exploiting the dynamic features caused by the natural fingerprint phenomena such as perspiration and elasticity. The evaluation results show an efficient capability to detect attacks where, in some configurations, the mechanisms are capable of eliminating some attack species and mitigating the rest of the species while keeping the user convenience at a high level.En las últimas décadas, hemos asistido a un despliegue a gran escala de los sistemas biométricos en diferentes aplicaciones de la vida cotidiana, sustituyendo a los métodos de reconocimiento tradicionales, como las contraseñas y los tokens. Actualmente los sistemas biométricos ya forman parte de nuestra vida cotidiana: es habitual emplear estos sistemas para que nos proporcionen acceso a nuestros dispositivos electrónicos (teléfonos inteligentes, tabletas, ordenadores portátiles, etc.) usando nuestras características biométricas. Además, accedemos a nuestras cuentas bancarias, realizamos diversos tipos de pagos y transacciones utilizando los sensores biométricos integrados en nuestros dispositivos. Por otra parte, diferentes organizaciones, empresas e instituciones utilizan soluciones basadas en la biometría para el control de acceso. A escala nacional, las autoridades policiales y de control fronterizo utilizan dispositivos de reconocimiento biométrico con fines de identificación y verificación individual. Por lo tanto, en todas estas aplicaciones se confía en que los sistemas biométricos proporcionen un reconocimiento seguro en el que solo el usuario genuino pueda ser reconocido como tal. Además, el sistema biométrico debe garantizar que un individuo no pueda ser identificado como otra persona. En el estado del arte, hay un número sorprendente de experimentos que muestran la posibilidad de robar las características biométricas de alguien, y utilizarlas para crear un rasgo biométrico artificial que puede ser utilizado por un atacante con el fin de reclamar la identidad del usuario genuino. También se han dado casos reales de personas que lograron engañar al sistema de reconocimiento biométrico en aeropuertos y teléfonos inteligentes [1]–[3]. Esto hace que sea necesario investigar estas posibles amenazas y proponer contramedidas que garanticen altos niveles de seguridad y comodidad para el usuario. En consecuencia, es vital la realización de evaluaciones de seguridad para identificar (1) los fallos de seguridad de los sistemas biométricos, (2) las posibles amenazas que pueden explotar estos fallos, y (3) las medidas que aumentan la seguridad del sistema biométrico reduciendo estas amenazas. La identificación de las vulnerabilidades del sistema lleva a proponer soluciones de seguridad adecuadas que ayuden a conseguir una mayor integridad. Esta tesis tiene como objetivo investigar la vulnerabilidad en los sistemas de modalidad de huella dactilar a los ataques de presentación en entornos no supervisados, para luego implementar mecanismos que permitan detectar dichos ataques y evitar el mal uso del sistema. Para lograr estos objetivos, la tesis se desarrolla en las siguientes tres fases. En la primera fase, se estudia el esquema del sistema biométrico genérico analizando sus puntos vulnerables con especial atención a los ataques de presentación. El estudio revisa la literatura sobre ataques de presentación y las soluciones correspondientes, es decir, los mecanismos de detección de ataques de presentación, para seis modalidades biométricas: huella dactilar, rostro, iris, vascular, firma manuscrita y voz. Además, se proporciona una nueva taxonomía para los mecanismos de detección de ataques de presentación. La taxonomía propuesta ayuda a comprender el problema de los ataques de presentación y la forma en que la literatura ha tratado de abordarlo. Esta taxonomía presenta un punto de partida para iniciar nuevas investigaciones que propongan novedosos mecanismos de detección de ataques de presentación. En la segunda fase, se desarrolla una metodología de evaluación a partir de dos fuentes: (1) la norma ISO/IEC 30107, y (2) Common Evaluation Methodology por el Common Criteria. La metodología desarrollada considera dos aspectos importantes del mecanismo de detección de ataques de presentación (1) la resistencia del mecanismo a los ataques de presentación, y (2) la correspondiente amenaza del ataque estudiado. Para el primer punto, se han de señalar las capacidades técnicas del mecanismo y cómo influyen en la seguridad y la facilidad de uso del sistema biométrico. Para el segundo aspecto se debe llevar a cabo una evaluación de la vulnerabilidad, teniendo en cuenta todos los factores que afectan al potencial de ataque. Por último, siguiendo esta metodología, se lleva a cabo una recogida de datos que incluye 7128 vídeos de huellas dactilares genuinas y de presentación de ataques. Los datos se recogen utilizando dos tecnologías de sensor, dos escenarios de presentación y considerando siete tipos de instrumentos de ataque. La base de datos se utiliza para desarrollar y evaluar mecanismos dinámicos de detección de ataques de presentación que explotan las características espacio-temporales de las huellas dactilares. En la fase final, se desarrolla un conjunto de mecanismos novedosos de detección de ataques de presentación que explotan las características dinámicas causadas por los fenómenos naturales de las huellas dactilares, como la transpiración y la elasticidad. Los resultados de la evaluación muestran una capacidad eficiente de detección de ataques en la que, en algunas configuraciones, los mecanismos son capaces de eliminar completamente algunos tipos de instrumentos de ataque y mitigar el resto de los tipos manteniendo la comodidad del usuario en un nivel alto.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Cristina Conde Vila.- Secretario: Mariano López García.- Vocal: Farzin Derav

Multibiometric security in wireless communication systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 05/08/2010.This thesis has aimed to explore an application of Multibiometrics to secured wireless communications. The medium of study for this purpose included Wi-Fi, 3G, and WiMAX, over which simulations and experimental studies were carried out to assess the performance. In specific, restriction of access to authorized users only is provided by a technique referred to hereafter as multibiometric cryptosystem. In brief, the system is built upon a complete challenge/response methodology in order to obtain a high level of security on the basis of user identification by fingerprint and further confirmation by verification of the user through text-dependent speaker recognition. First is the enrolment phase by which the database of watermarked fingerprints with memorable texts along with the voice features, based on the same texts, is created by sending them to the server through wireless channel. Later is the verification stage at which claimed users, ones who claim are genuine, are verified against the database, and it consists of five steps. Initially faced by the identification level, one is asked to first present one’s fingerprint and a memorable word, former is watermarked into latter, in order for system to authenticate the fingerprint and verify the validity of it by retrieving the challenge for accepted user. The following three steps then involve speaker recognition including the user responding to the challenge by text-dependent voice, server authenticating the response, and finally server accepting/rejecting the user. In order to implement fingerprint watermarking, i.e. incorporating the memorable word as a watermark message into the fingerprint image, an algorithm of five steps has been developed. The first three novel steps having to do with the fingerprint image enhancement (CLAHE with 'Clip Limit', standard deviation analysis and sliding neighborhood) have been followed with further two steps for embedding, and extracting the watermark into the enhanced fingerprint image utilising Discrete Wavelet Transform (DWT). In the speaker recognition stage, the limitations of this technique in wireless communication have been addressed by sending voice feature (cepstral coefficients) instead of raw sample. This scheme is to reap the advantages of reducing the transmission time and dependency of the data on communication channel, together with no loss of packet. Finally, the obtained results have verified the claims