Visual identification by signature tracking

We propose a new camera-based biometric: visual signature identification. We discuss the importance of the parameterization of the signatures in order to achieve good classification results, independently of variations in the position of the camera with respect to the writing surface. We show that affine arc-length parameterization performs better than conventional time and Euclidean arc-length ones. We find that the system verification performance is better than 4 percent error on skilled forgeries and 1 percent error on random forgeries, and that its recognition performance is better than 1 percent error rate, comparable to the best camera-based biometrics

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

웨어러블 센서 및 에너지 소자의 공간 신호 및 열 전달 증진을 위한 나노복합체를 이용한 기계적 순응성 향상

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2020. 8. 홍용택.Electronic skin (e-skin) that mimics mechanical and functional properties of human skin has a strong impact on the field of wearable electronics. Beyond being just wearable, e-skin seamlessly interfaces human, machine, and environment by perfectly adhering to soft and time-dynamic three-dimensional (3D) geometries of human skin and organs. Real-time and intimate access to the sources of physical and biological signals can be achieved by adopting soft or flexible electronic sensors that can detect pressure, strain, temperature, and chemical substances. Such extensions in accessible signals drastically accelerate the growth of the Internet of Things (IoT) and expand its application to health monitoring, medical implants, and novel human-machine interfaces. In wearable sensors and energy devices, which are essential building blocks for skin-like functionalities and self-power generation in e-skin, spatial signals and heat are transferred from time-dynamic 3D environments through numerous geometries and electrical devices. Therefore, the transfer of high-fidelity signals or a large amount of heat is of great importance in these devices. The mechanical conformability potentially enhances the signal/heat transfer by providing conformal geometries with the 3D sources. However, while the relation between system conformability and electrical signals has been widely investigated, studies on its effect on the transfer of other mechanical signals and heat remain in their early stages. Furthermore, because active materials and their designs for sensors and energy devices have been optimized to maximize their performances, it is challenging to develop ultrathin or soft forms of active layers without compromising their performances. Therefore, many devices in these fields suffer from poor spatial signal/heat transfer due to limited conformability. In this dissertation, to ultimately augment the functionalities of wearable sensors and energy devices, comprehensive studies on conformability enhancement via composite materials and its effect on signal/heat transfer, especially in pressure sensors and thermoelectric generators (TEGs), are conducted. A solution for each device is carefully optimized to reinforce its conformability, taking account of the structure, characteristics, and potential advantages of the device. As a result, the mechanical conformability of each device is significantly enhanced, improving signal/heat transfer and consequently augmenting its functionalities, which have been considered as tough challenges in each area. The effect of the superior conformability on signal/heat transfer is systematically analyzed via a series of experiments and finite element analyses. Demonstrations of practical wearable electronics show the feasibility of the proposed strategies. For wearable pressure sensors, ultrathin piezoresistive layers are developed using cellulose/nanowire nanocomposites (CNNs). The unique nanostructured surface enables unprecedentedly high sensor performances such as ultrahigh sensitivity, wide working range, and fast response time without microstructures in sensing layers. Because the ultrathin pressure sensor perfectly conforms to 3D contact objects, it transfers pressure distribution into conductivity distribution with high spatial fidelity. When integrated with a quantum dot-based electroluminescent film, the transferred high-resolution pressure distribution is directly visualized without the need for pixel structures. The electroluminescent skin enables real-time smart touch interfaces that can identify the user as well as touch force and location. For high-performance wearable TEGs, an intrinsically soft heat transfer and electrical interconnection platform (SHEP) is developed. The SHEP comprises AgNW random networks for intrinsically stretchable electrodes and magnetically self-assembled metal particles for soft thermal conductors (STCs). The stretchable electrodes lower the flexural rigidity, and the STCs enhance the heat exchange capability of the soft platform, maintaining its softness. As a result, a compliant TEG with SHEPs forms unprecedentedly conformal contact with 3D heat sources, thereby enhancing the heat transfer to the TE legs. This results in significant improvement in thermal energy harvesting on 3D surfaces. Self-powered wearable warning systems indicating an abrupt temperature increase with light-emitting alarms are demonstrated to show the feasibility of this strategy. This study provides a systematic and comprehensive framework for enhancing mechanical conformability of e-skin and consequently improving the transfer of spatial signals and energy from time-dynamic and complex 3D surfaces. The framework can be universally applied to other fields in wearable electronics that require improvement in signal/energy transfer through conformal contact with 3D surfaces. The materials, manufacturing methods, and devices introduced in this dissertation will be actively exploited in practical and futuristic applications of wearable electronics such as skin-attachable advanced user interfaces, implantable bio-imaging systems, nervous systems in soft robotics, and self-powered artificial tactile systems.인간 피부의 기계적 특성 및 기능을 모방하는 전자피부(electronic skin, e-skin)는 웨어러블 전자기기 분야의 트렌드를 바꾸고 있다. 기존의 웨어러블 전자기기가 단지 착용하는데 그쳤다면, 전자피부는 인간의 피부와 장기 표면에 완벽하게 붙어 동작함으로써 기존에는 접근 불가능 했던 다양한 생체 신호를 높은 신뢰도로 감지하고 처리할 수 있다. 실시간으로 감지 가능한 생체 신호의 확장은 사물인터넷(Internet of Things, IoT)의 성장을 획기적으로 가속화하고 헬스케어, 의료용 임플란트, 소프트 로봇 및 새로운 휴먼 머신 인터페이스로의 응용을 가능하게 한다. 전자피부의 필수요소인 센서와 에너지 소자에서는 삼차원 표면의 공간신호와 열에너지를 손실 없이 전달하는 것이 매우 중요하다. 이러한 공간 신호와 열에너지는 다양한 기하 구조와 전자소자를 거쳐 처리 가능한 신호로 전달된다. 이 과정에서 3차원 표면에 빈틈없이 붙는 기계적 순응성(mechanical conformability)은 공간신호와 열에너지를 왜곡 없이 전달하는 것을 가능하게 한다. 전자피부의 기계적 순응성을 증가시키는 방법은 크게 다음과 같이 두 가지로 나눌 수 있다. (1) 전자피부를 두께를 낮추는 전략과 (2) 전자피부의 영률(Youngs modulus)을 낮추어 고무와 같이 부드럽게 만드는 전략이다. 하지만, 기존 센서 및 에너지 소자를 위한 재료와 디자인이 각 장치의 성능을 향상시키는 것에 초점이 맞추어져 있기 때문에, 고성능을 유지하면서 매우 얇거나 연질 형태의 소자를 개발하는 것은 매우 도전적이었다. 따라서 고유연성을 확보하지 못한 기존 센서와 에너지 소자는 공간 신호 및 열 전달이 심각하게 저해되고, 이로 인해 공간 압력의 왜곡, 열전 효율의 저하와 같은 한계를 보여준다. 이 논문에서는 웨어러블 센서와 에너지 소자의 비약적인 기능 향상을 궁극적인 목표로, 각 소자에 최적화된 재료와 제작방식, 구조를 이용해 이들의 기계적 순응성을 획기적으로 높이고, 이를 통한 공간 신호 및 열 전달의 향상을 심도 있게 분석한다. 특히, 두께를 낮추거나 영률을 낮추는 두 가지 전략 중 각 소자에 가장 적합한 전략을 선택하고, 체계적인 방법론을 적용하여 이들의 기계적 순응성과 공간 신호 및 열 전달을 증진시킨다. 이 과정에서 나노융복합재료가 각 전략을 구현하는 핵심 요소로 작용한다. 각 소자에 따른 구체적인 연구 내용은 다음과 같다. 첫째, 압력 센서의 경우 초박막 셀룰로오스/나노와이어 복합체를 이용하여 고성능의 저항방식 압력 센서를 개발한다. 이러한 복합체는 표면에 형성된 고유한 나노구조 덕분에 마이크로구조체를 이용한 기존 압력 센서보다 월등한 성능을 보여준다. 특히, 1 마이크로 미터 수준의 매우 얇은 두께로 인해 접촉 물체의 복잡한 형상에 완벽하게 순응할 수 있고, 이로 인해 고해상도 압력 분포를 왜곡 없이 저항 분포로 전달할 수 있다. 이러한 압력 센서를 양자 점 발광소자와 결합하여 고해상도의 압력분포를 높은 정밀도로 이미징 가능한 발광 소자를 보고한다. 둘째, 열전 소자의 경우 기존의 금속 전극으로 인한 낮은 유연성과 탄성중합체의 낮은 열 전도도를 극복하기 위해 열 전달 능력이 획기적으로 향상된 낮은 영률의 소프트 전극 플랫폼을 개발한다. 소프트 플랫폼은 내부에 은 나노와이어 기반의 신축성 전극을 갖고 있으며, 자기장을 통해 자가 정렬된 금속 입자들이 효과적으로 외부 열을 열전 재료에 전달한다. 이를 기반으로 제작된 고유연성 열전 소자는 삼차원 열원에 빈틈없이 붙어 열 손실을 최소화 하며, 이로 인해 높은 열전 효율을 달성한다. 이 논문은 다양한 전자소자의 유연성을 증진시키고 이를 통한 공간 신호 및 열 전달의 향상을 도모하고 분석하는 체계적이고 종합적인 방법론을 제시했다는 데 큰 의의가 있다. 제안된 방법론은 분야에 국한되지 않고 다양한 소자의 개발에 적용할 수 있어 웨어러블 기기와 전자피부 분야의 기계적, 기능적 발전에 크게 기여할 것으로 기대된다. 뿐만 아니라 이 연구에서 최초로 개발한 소재 및 소자들은 다양한 웨어러블 어플리케이션과 산업에 곧바로 융합되고 응용될 수 있다. 이를 통해 신체 부착 및 삽입 가능한 생체 이미징 시스템, 소프트 로봇을 위한 신경 체계, 자가 발전이 가능한 인공 감각 기관, 가상 및 증강 현실을 위한 새로운 유저 인터페이스와 같은 미래 지향적 융합 어플리케이션의 실현을 앞당길 것으로 기대된다.Chapter 1. Introduction 1 1.1 Wearable Electronics and Electronic Skin 1 1.2 Mechanical Conformability of Electronic Skin 6 1.2.1 Definition and Advantages 6 1.2.2 Thickness-Based Conformability 11 1.2.3 Softness-Based Conformability 15 1.3 Conformability for Enhanced Signal/Heat Transfer in Wearable Sensors and Energy Devices 19 1.3.1 Conformability for Spatial Signal Transfer in Pressure Sensors 20 1.3.2 Conformability for Heat Transfer in Thermoelectric Generators 22 1.4 Motivation and Organization of This Dissertation 24 Chapter 2. Ultrathin Cellulose Nanocomposites for High-Performance Piezoresistive Pressure Sensors 28 2.1 Introduction 28 2.2 Experimental Section 31 2.2.1 Fabrication of the CNNs and Pressure Sensors 31 2.2.2 Measurements 34 2.3 Results and Discussion 38 2.3.1 Morphology of CNNs 38 2.3.2 Piezoresistive Characteristics of CNNs 41 2.3.3 Mechanism of High Sensitivity and Great Linearity 45 2.3.4 Fast Response Time of CNN-Based Pressure Sensors 49 2.3.5 Cyclic Reliability of CNN-Based Pressure Sensors 53 2.3.6 Mechanical Reliability and Conformability 57 2.3.7 Temperature and Humidity Tolerance 63 2.4 Conclusion 66 Chapter 3. Ultraflexible Electroluminescent Skin for High-Resolution Imaging of Pressure Distribution 67 3.1 Introduction 67 3.2 Main Concept 70 3.3 Experimental Section 72 3.3.1 Fabrication of Pressure-Sensitive Photonic Skin 72 3.3.2 Characterization of Photonic Skin 74 3.4 Results and Discussion 76 3.4.1 Structure and Morphology of Photonic Skin 76 3.4.2 Pressure Response of Photonic Skin 79 3.4.3 Effect of Conformability on Spatial Resolution 85 3.4.4 Demonstration of High-Resolution Pressure Imaging 99 3.4.5 Pressure Data Acquisition 104 3.4.6 Application to Smart Touch Interfaces 106 3.5 Conclusion 109 Chapter 4. Intrinsically Soft Heat Transfer and Electrical Interconnection Platforms Using Magnetic Nanocomposites 110 4.1 Introduction 110 4.2 Experimental Section 115 4.2.1 Fabrication of SHEPs 115 4.2.2 Measurements 117 4.3 Results and Discussion 119 4.3.1 Fabrication Scheme and Morphology of SHEPs 119 4.3.2 Calculation of Particle Concentration in STCs 124 4.3.3 Enhancement of Heat Transfer Ability via Magnetic Self-Assembly 127 4.3.4 Softness of STCs 131 4.3.5 Mechanical Reliability of Stretchable Electrodes 133 4.3.6 Optimization of Magnetic Self-Assembly Process 135 4.4 Conclusion 139 Chapter 5. Highly Conformable Thermoelectric Generators with Enhanced Heat Transfer Ability 140 5.1 Introduction 140 5.2 Experimental Section 142 5.2.1 Fabrication of Compliant TEGs 142 5.2.2 Measurements 144 5.2.3 Finite Element Analysis 147 5.3 Results and Discussion 149 5.3.1 Enhancement of TE Performance via STCs 149 5.3.2 Mechanical Reliability of Compliant TEGs 157 5.3.3 Enhanced TE Performance on 3D Surfaces via Conformability 162 5.3.4 Self-Powered Wearable Applications 167 5.4 Conclusion 171 Chapter 6. Summary, Limitations, and Recommendations for Future Researches 172 6.1 Summary and Conclusion 172 6.2 Limitations and Recommendations 176 6.2.1 Pressure Sensors and Photonic Skin 176 6.2.2 Compliant TEGs 177 Bibliography 178 Publication List 186 Abstract in Korean 192Docto

Unifying the Visible and Passive Infrared Bands: Homogeneous and Heterogeneous Multi-Spectral Face Recognition

Face biometrics leverages tools and technology in order to automate the identification of individuals. In most cases, biometric face recognition (FR) can be used for forensic purposes, but there remains the issue related to the integration of technology into the legal system of the court. The biggest challenge with the acceptance of the face as a modality used in court is the reliability of such systems under varying pose, illumination and expression, which has been an active and widely explored area of research over the last few decades (e.g. same-spectrum or homogeneous matching). The heterogeneous FR problem, which deals with matching face images from different sensors, should be examined for the benefit of military and law enforcement applications as well. In this work we are concerned primarily with visible band images (380-750 nm) and the infrared (IR) spectrum, which has become an area of growing interest.;For homogeneous FR systems, we formulate and develop an efficient, semi-automated, direct matching-based FR framework, that is designed to operate efficiently when face data is captured using either visible or passive IR sensors. Thus, it can be applied in both daytime and nighttime environments. First, input face images are geometrically normalized using our pre-processing pipeline prior to feature-extraction. Then, face-based features including wrinkles, veins, as well as edges of facial characteristics, are detected and extracted for each operational band (visible, MWIR, and LWIR). Finally, global and local face-based matching is applied, before fusion is performed at the score level. Although this proposed matcher performs well when same-spectrum FR is performed, regardless of spectrum, a challenge exists when cross-spectral FR matching is performed. The second framework is for the heterogeneous FR problem, and deals with the issue of bridging the gap across the visible and passive infrared (MWIR and LWIR) spectrums. Specifically, we investigate the benefits and limitations of using synthesized visible face images from thermal and vice versa, in cross-spectral face recognition systems when utilizing canonical correlation analysis (CCA) and locally linear embedding (LLE), a manifold learning technique for dimensionality reduction. Finally, by conducting an extensive experimental study we establish that the combination of the proposed synthesis and demographic filtering scheme increases system performance in terms of rank-1 identification rate

Human metrology for person classification and recognition

Human metrological features generally refers to geometric measurements extracted from humans, such as height, chest circumference or foot length. Human metrology provides an important soft biometric that can be used in challenging situations, such as person classification and recognition at a distance, where hard biometric traits such as fingerprints and iris information cannot easily be acquired. In this work, we first study the question of predictability and correlation in human metrology. We show that partial or available measurements can be used to predict other missing measurements. We then investigate the use of human metrology for the prediction of other soft biometrics, viz. gender and weight. The experimental results based on our proposed copula-based model suggest that human body metrology contains enough information for reliable prediction of gender and weight. Also, the proposed copula-based technique is observed to reduce the impact of noise on prediction performance. We then study the question of whether face metrology can be exploited for reliable gender prediction. A new method based solely on metrological information from facial landmarks is developed. The performance of the proposed metrology-based method is compared with that of a state-of-the-art appearance-based method for gender classification. Results on several face databases show that the metrology-based approach resulted in comparable accuracy to that of the appearance-based method. Furthermore, we study the question of person recognition (classification and identification) via whole body metrology. Using CAESAR 1D database as baseline, we simulate intra-class variation with various noise models. The experimental results indicate that given enough number of features, our metrology-based recognition system can have promising performance that is comparable to several recent state-of-the-art recognition systems. We propose a non-parametric feature selection methodology, called adapted k-nearest neighbor estimator, which does not rely on intra-class distribution of the query set. This leads to improved results over other nearest neighbor estimators (as feature selection criteria) for moderate number of features. Finally we quantify the discrimination capability of human metrology, from both individuality and capacity perspectives. Generally, a biometric-based recognition technique relies on an assumption that the given biometric is unique to an individual. However, the validity of this assumption is not yet generally confirmed for most soft biometrics, such as human metrology. In this work, we first develop two schemes that can be used to quantify the individuality of a given soft-biometric system. Then, a Poisson channel model is proposed to analyze the recognition capacity of human metrology. Our study suggests that the performance of such a system depends more on the accuracy of the ground truth or training set

Forensic Science: \u3ci\u3eDaubert\u27s\u3c/i\u3e Failure

In 2015, a federal judge noted that “[m]any defendants have been convicted and spent countless years in prison based on evidence by arson experts who were later shown to be little better than witch doctors.” In the same year, a White House science advisor observed: “Suggesting that bite marks [should] still be a seriously used technology is not based on science, on measurement, on something that has standards, but more of a gut-level reaction.” According to another judge “[a]s matters currently stand, a certainty statement regarding toolmark pattern matching has the same probative value as the vision of a psychic.” A recent New York Times editorial echoed these sentiments: “And the courts have only made the problem worse by purporting to be scientifically literate, and allowing in all kinds of evidence that would not make it within shouting distance of a peer-reviewed journal. Of the 329 exonerations based on DNA testing since 1989, more than one-quarter involved convictions based on ‘pattern’ evidence — like hair samples, ballistics, tire tracks, and bite marks — testified to by so-called experts.” These criticisms are valid — which raises a puzzling and consequential question: Why didn’t the Supreme Court’s “junk science” decision, Daubert v. Merrell Dow Pharmaceuticals, Inc., prevent or restrict the admissibility of testimony based on flawed forensic techniques? Daubert was decided in 1993, nearly twenty-five years ago. This article examines the justice system’s failure by reviewing the status of several forensic techniques: (1) bite mark analysis, (2) microscopic hair comparisons, (3) firearms and toolmark identifications, (4) fingerprint examinations, (5) bullet lead analysis, and (6) arson investigations. It argues that the system’s failure can be traced back to its inability to demand and properly evaluate foundational research, i.e., Daubert’s first factor (empirical testing), and concludes that the courts may be institutionally incapable of applying Daubert in criminal cases. A different paradigm is needed, one that assigns an independent agency the responsibility of evaluating foundational research. This approach was recently recommended by the National Commission on Forensic Science and the President’s Council of Advisors on Science and Technology. Both recommended that the National Institute of Standards and Technology evaluate all forensic disciplines on a continuing basis, thereby injecting much needed scientific expertise into the process

Evaluation and analysis of hybrid intelligent pattern recognition techniques for speaker identification

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The rapid momentum of the technology progress in the recent years has led to a tremendous rise in the use of biometric authentication systems. The objective of this research is to investigate the problem of identifying a speaker from its voice regardless of the content (i.e. text-independent), and to design efficient methods of combining face and voice in producing a robust authentication system. A novel approach towards speaker identification is developed using wavelet analysis, and multiple neural networks including Probabilistic Neural Network (PNN), General Regressive Neural Network (GRNN)and Radial Basis Function-Neural Network (RBF NN) with the AND voting scheme. This approach is tested on GRID and VidTIMIT cor-pora and comprehensive test results have been validated with state- of-the-art approaches. The system was found to be competitive and it improved the recognition rate by 15% as compared to the classical Mel-frequency Cepstral Coe±cients (MFCC), and reduced the recognition time by 40% compared to Back Propagation Neural Network (BPNN), Gaussian Mixture Models (GMM) and Principal Component Analysis (PCA). Another novel approach using vowel formant analysis is implemented using Linear Discriminant Analysis (LDA). Vowel formant based speaker identification is best suitable for real-time implementation and requires only a few bytes of information to be stored for each speaker, making it both storage and time efficient. Tested on GRID and Vid-TIMIT, the proposed scheme was found to be 85.05% accurate when Linear Predictive Coding (LPC) is used to extract the vowel formants, which is much higher than the accuracy of BPNN and GMM. Since the proposed scheme does not require any training time other than creating a small database of vowel formants, it is faster as well. Furthermore, an increasing number of speakers makes it di±cult for BPNN and GMM to sustain their accuracy, but the proposed score-based methodology stays almost linear. Finally, a novel audio-visual fusion based identification system is implemented using GMM and MFCC for speaker identi¯cation and PCA for face recognition. The results of speaker identification and face recognition are fused at different levels, namely the feature, score and decision levels. Both the score-level and decision-level (with OR voting) fusions were shown to outperform the feature-level fusion in terms of accuracy and error resilience. The result is in line with the distinct nature of the two modalities which lose themselves when combined at the feature-level. The GRID and VidTIMIT test results validate that the proposed scheme is one of the best candidates for the fusion of face and voice due to its low computational time and high recognition accuracy

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