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PART I: INTRODUCTION AND INTERDISCIPLINARY APPROACHES. The interplay of privacy, security and user-determination in biometrics / Claus Vielhauer -- Privacy of online handwriting biometrics related to biomedical analysis / Marcos Faundez-Zanuy and Jiri Mekyska -- Privacy concepts in biometrics : lessons learned from forensics / Jana Dittmann and Christian Kraetzer -- PART II: PRIVACY AND SECURITY OF BIOMETRICS WITHIN GENERAL SECURITY SYSTEMS. Physical layer security : biometrics vs. physical objects / Svyatoslav Voloshynovskiy, Taras Holotyak, and Maurits Diephuis -- Biometric systems in unsupervised environments and smart cards : conceptual advances on privacy and security / Raul Sanchez-Reillo -- Inverse biometrics and privacy / Maria Gomez-Barrero and Javier Galbally -- Double-layer secret-sharing system involving privacy preserving biometric authentication / Quang Nhat Tran, Song Wang, Ruchong Ou and Jiankun Hu -- PART III: SECURITY AND PRIVACY ISSUES INHERENT TO BIOMETRICS. Biometric template protection : state-of-the-art, issues and challenges / Christian Rathgeb and Christoph Busch -- Handwriting biometrics : feature-based optimisation / Tobias Scheidat -- Presentation attack detection in voice biometrics / Pavel Korshunov and Sébastien Marcel -- Benford's law for classification of biometric images / Aamo Iorliam, Anthony T.S. Ho, Norman Poh, Xi Zhao and Zhe Xia -- PART IV. USER-CENTRICITY AND THE FUTURE. Random projections for increased privacy / Sabah Jassim -- 13 De-identification for privacy protection in biometrics / Slobodan Ribarić and Nikola Pavešić -- Secure cognitive recognition : brain-based biometric cryptosystems using EEG / Emanuele Maiorana and Patrizio Campisi -- A multidisciplinary analysis of the implementation of biometric systems and their implications in society / Vassiliki Andronikou, Stefanos Xefteris, Theodora Varvarigou, and Panagiotis Bamidis -- Biometrics, identity, recognition and the private sphere where we are, where we go / Emlio Mordini

Biometric recognition, or simply biometrics, is a rapidly evolving field with applications ranging from accessing one's computer, to gaining entry into a country. Biometric systems rely on the use of physical or behavioral traits, such as fingerprints, face, voice and hand geometry, to establish the identity of an individual. The deployment of large-scale biometric systems in both commercial (e.g., grocery stores, amusement parks, airports) and government (e.g., US-VISIT) applications, increases the public's awareness of this technology. This rapid growth also highlights the challenges associated with designing and deploying biometric systems. Indeed, the problem of biometric recognition is a grand challenge in its own right. The past five years have seen a significant growth in biometric research resulting in the development of innovative sensors, robust and efficient algorithms for feature extraction and matching, enhanced test methodologies and novel applications. These advances have resulted in robust, accurate, secure and cost effective biometric systems. The Handbook of Biometrics -- an edited volume by prominent invited researchers in biometrics -- describes the fundamentals as well as the latest advancements in the burgeoning field of biometrics. It is designed for professionals, practitioners and researchers in biometrics, pattern recognition and computer security. The Handbook of Biometrics can be used as a primary textbook for an undergraduate biometrics class. This book is also suitable as a secondary textbook or reference for advanced-level students in computer science.

International audience ; Speech recordings are a rich source of personal, sensitive data that can be used to support a plethora of diverse applications, from health profiling to biometric recognition. It is therefore essential that speech recordings are adequately protected so that they cannot be misused. Such protection, in the form of privacy-preserving technologies, is required to ensure that: (i) the biometric profiles of a given individual (e.g., across different biometric service operators) are unlinkable; (ii) leaked, encrypted biometric information is irreversible, and that (iii) biometric references are renewable. Whereas many privacy-preserving technologies have been developed for other biometric characteristics, very few solutions have been proposed to protect privacy in the case of speech signals. Despite privacy preservation this is now being mandated by recent European and international data protection regulations. With the aim of fostering progress and collaboration between researchers in the speech, biometrics and applied cryptography communities, this survey article provides an introduction to the field, starting with a legal perspective on privacy preservation in the case of speech data. It then establishes the requirements for effective privacy preservation, reviews generic cryptography-based solutions, followed by specific techniques that are applicable to speaker characterisation (biometric applications) and speech characterisation (non-biometric applications). Glancing at non-biometrics, methods are presented to avoid function creep, preventing the exploitation of biometric information, e.g., to single out an identity in speech-assisted health care via I Recent advances in speaker and language recognition and characterisation.

International audience ; Speech recordings are a rich source of personal, sensitive data that can be used to support a plethora of diverse applications, from health profiling to biometric recognition. It is therefore essential that speech recordings are adequately protected so that they cannot be misused. Such protection, in the form of privacy-preserving technologies, is required to ensure that: (i) the biometric profiles of a given individual (e.g., across different biometric service operators) are unlinkable; (ii) leaked, encrypted biometric information is irreversible, and that (iii) biometric references are renewable. Whereas many privacy-preserving technologies have been developed for other biometric characteristics, very few solutions have been proposed to protect privacy in the case of speech signals. Despite privacy preservation this is now being mandated by recent European and international data protection regulations. With the aim of fostering progress and collaboration between researchers in the speech, biometrics and applied cryptography communities, this survey article provides an introduction to the field, starting with a legal perspective on privacy preservation in the case of speech data. It then establishes the requirements for effective privacy preservation, reviews generic cryptography-based solutions, followed by specific techniques that are applicable to speaker characterisation (biometric applications) and speech characterisation (non-biometric applications). Glancing at non-biometrics, methods are presented to avoid function creep, preventing the exploitation of biometric information, e.g., to single out an identity in speech-assisted health care via I Recent advances in speaker and language recognition and characterisation.

International audience ; Speech recordings are a rich source of personal, sensitive data that can be used to support a plethora of diverse applications, from health profiling to biometric recognition. It is therefore essential that speech recordings are adequately protected so that they cannot be misused. Such protection, in the form of privacy-preserving technologies, is required to ensure that: (i) the biometric profiles of a given individual (e.g., across different biometric service operators) are unlinkable; (ii) leaked, encrypted biometric information is irreversible, and that (iii) biometric references are renewable. Whereas many privacy-preserving technologies have been developed for other biometric characteristics, very few solutions have been proposed to protect privacy in the case of speech signals. Despite privacy preservation this is now being mandated by recent European and international data protection regulations. With the aim of fostering progress and collaboration between researchers in the speech, biometrics and applied cryptography communities, this survey article provides an introduction to the field, starting with a legal perspective on privacy preservation in the case of speech data. It then establishes the requirements for effective privacy preservation, reviews generic cryptography-based solutions, followed by specific techniques that are applicable to speaker characterisation (biometric applications) and speech characterisation (non-biometric applications). Glancing at non-biometrics, methods are presented to avoid function creep, preventing the exploitation of biometric information, e.g., to single out an identity in speech-assisted health care via I Recent advances in speaker and language recognition and characterisation.

An insight into the biometric industry and the steps for successful deployment Biometrics technologies verify identity through characteristics such as fingerprints, voices, and faces. By providing increased security and convenience, biometrics have begun to see widespread deployment in network, e-commerce, and retail applications. This book provides in-depth analysis of biometrics as a solution for authenticating employees and customers. Leading authority, Samir Nanavati explores privacy, security, accuracy, system design, user perceptions, and lessons learned in biometric deployments. He also

The Qur'an is the holy book which is inexhaustibly studied by many people. It has inspired a lot of thoughts, research and studies. The discussions of human in the Qur'an are numerous, ranging from the creation, life, naming, relationships of one another, and etc. The Qur'an mentions that man is created in the perfect form, which is given advantages over other creatures. With his intellectual power, human tries to explore knowledge about him and understand his nature as a creature of God. Biometrics views human as a unique being. Biometrics sees that parts of the human body can be used as a security device because each human has his special uniqueness different from one another. Because of this specificity, human invents devices that refer to all materials, types of equipment, labours, and man-made systems to replicate the existing systems in nature. At present, the scientific community really needs such a device, especially in the field of nanotechnology, robot technology, artificial intelligence, medicine and military. Biometric normally used in the form of authentification, including Introduction to Fingerprint, Face Recognition, Recognition Retina or Iris, Geometry Arm, Geometry Finger, introduction of Palms, Voice Recognition, Introduction to Signatures, DNA (Deoxyribonucleic Acid), Thermal Imaging (Body Temperature), Shape Ear, Body Odor, Body Movement. On some types of biometric authentication on top of the al-Qur'an gives a signal on Surah Fussilat [41]: 20-22.

Digitalisation, which involves 'leveraging digitisation to improve business processes' has permeated a plethora of sectors. Humanitarian aid is no exception. One such subset of digitalisation is biometric verification: a technological means used to identify a person based on their biological features, including but not limited to 'fingerprints, hand and earlobe geometries, retina patterns, voice prints … Continued

Biometrics is defined by the International Organization for Standardization (ISO) as "the automated recognition of individuals based on their behavioral and biological characteristics" Examples of distinctive features evaluated by biometrics, called biometric traits, are behavioral characteristics like the signature, gait, voice, and keystroke, and biological characteristics like the fingerprint, face, iris, retina, hand geometry, palmprint, ear, and DNA. The biometric recognition is the process that permits to establish the identity of a person, and can be performed in two modalities: verification, and identification. The verification modality evaluates if the identity declared by an individual corresponds to the acquired biometric data. Differently, in the identification modality, the recognition application has to determine a person's identity by comparing the acquired biometric data with the information related to a set of individuals. Compared with traditional techniques used to establish the identity of a person, biometrics offers a greater confidence level that the authenticated individual is not impersonated by someone else. Traditional techniques, in fact, are based on surrogate representations of the identity, like tokens, smart cards, and passwords, which can easily be stolen or copied with respect to biometric traits. This characteristic permitted a wide diffusion of biometrics in different scenarios, like physical access control, government applications, forensic applications, logical access control to data, networks, and services. Most of the biometric applications, also called biometric systems, require performing the acquisition process in a highly controlled and cooperative manner. In order to obtain good quality biometric samples, the acquisition procedures of these systems need that the users perform deliberate actions, assume determinate poses, and stay still for a time period. Limitations regarding the applicative scenarios can also be present, for example the necessity of specific light and environmental conditions. Examples of biometric technologies that traditionally require constrained acquisitions are based on the face, iris, fingerprint, and hand characteristics. Traditional face recognition systems need that the users take a neutral pose, and stay still for a time period. Moreover, the acquisitions are based on a frontal camera and performed in controlled light conditions. Iris acquisitions are usually performed at a distance of less than 30 cm from the camera, and require that the user assume a defined pose and stay still watching the camera. Moreover they use near infrared illumination techniques, which can be perceived as dangerous for the health. Fingerprint recognition systems and systems based on the hand characteristics require that the users touch the sensor surface applying a proper and uniform pressure. The contact with the sensor is often perceived as unhygienic and/or associated to a police procedure. This kind of constrained acquisition techniques can drastically reduce the usability and social acceptance of biometric technologies, therefore decreasing the number of possible applicative contexts in which biometric systems could be used. In traditional fingerprint recognition systems, the usability and user acceptance are not the only negative aspects of the used acquisition procedures since the contact of the finger with the sensor platen introduces a security lack due to the release of a latent fingerprint on the touched surface, the presence of dirt on the surface of the finger can reduce the accuracy of the recognition process, and different pressures applied to the sensor platen can introduce non-linear distortions and low-contrast regions in the captured samples. Other crucial aspects that influence the social acceptance of biometric systems are associated to the privacy and the risks related to misuses of biometric information acquired, stored and transmitted by the systems. One of the most important perceived risks is related to the fact that the persons consider the acquisition of biometric traits as an exact permanent filing of their activities and behaviors, and the idea that the biometric systems can guarantee recognition accuracy equal to 100\% is very common. Other perceived risks consist in the use of the collected biometric data for malicious purposes, for tracing all the activities of the individuals, or for operating proscription lists. In order to increase the usability and the social acceptance of biometric systems, researchers are studying less-constrained biometric recognition techniques based on different biometric traits, for example, face recognition systems in surveillance applications, iris recognition techniques based on images captured at a great distance and on the move, and contactless technologies based on the fingerprint and hand characteristics. Other recent studies aim to reduce the real and perceived privacy risks, and consequently increase the social acceptance of biometric technologies. In this context, many studies regard methods that perform the identity comparison in the encrypted domain in order to prevent possible thefts and misuses of biometric data. The objective of this thesis is to research approaches able to increase the usability and social acceptance of biometric systems by performing less-constrained and highly accurate biometric recognitions in a privacy compliant manner. In particular, approaches designed for high security contexts are studied in order improve the existing technologies adopted in border controls, investigative, and governmental applications. Approaches based on low cost hardware configurations are also researched with the aim of increasing the number of possible applicative scenarios of biometric systems. The privacy compliancy is considered as a crucial aspect in all the studied applications. Fingerprint is specifically considered in this thesis, since this biometric trait is characterized by high distinctivity and durability, is the most diffused trait in the literature, and is adopted in a wide range of applicative contexts. The studied contactless biometric systems are based on one or more CCD cameras, can use two-dimensional or three-dimensional samples, and include privacy protection methods. The main goal of these systems is to perform accurate and privacy compliant recognitions in less-constrained applicative contexts with respect to traditional fingerprint biometric systems. Other important goals are the use of a wider fingerprint area with respect to traditional techniques, compatibility with the existing databases, usability, social acceptance, and scalability. The main contribution of this thesis consists in the realization of novel biometric systems based on contactless fingerprint acquisitions. In particular, different techniques for every step of the recognition process based on two-dimensional and three-dimensional samples have been researched. Novel techniques for the privacy protection of fingerprint data have also been designed. The studied approaches are multidisciplinary since their design and realization involved optical acquisition systems, multiple view geometry, image processing, pattern recognition, computational intelligence, statistics, and cryptography. The implemented biometric systems and algorithms have been applied to different biometric datasets describing a heterogeneous set of applicative scenarios. Results proved the feasibility of the studied approaches. In particular, the realized contactless biometric systems have been compared with traditional fingerprint recognition systems, obtaining positive results in terms of accuracy, usability, user acceptability, scalability, and security. Moreover, the developed techniques for the privacy protection of fingerprint biometric systems showed satisfactory performances in terms of security, accuracy, speed, and memory usage.

Speech recordings are a rich source of personal, sensitive data that can be used to support a plethora of diverse applications, from health profiling to biometric recognition. It is therefore essential that speech recordings are adequately protected so that they cannot be misused. Such protection, in the form of privacy-preserving technologies, is required to ensure that: (i) the biometric profiles of a given individual (e.g., across different biometric service operators) are unlinkable; (ii) leaked, encrypted biometric information is irreversible, and that (iii) biometric references are renewable. Whereas many privacy-preserving technologies have been developed for other biometric characteristics, very few solutions have been proposed to protect privacy in the case of speech signals. Despite privacy preservation this is now being mandated by recent European and international data protection regulations. With the aim of fostering progress and collaboration between researchers in the speech, biometrics and applied cryptography communities, this survey article provides an introduction to the field, starting with a legal perspective on privacy preservation in the case of speech data. It then establishes the requirements for effective privacy preservation, reviews generic cryptography-based solutions, followed by specific techniques that are applicable to speaker characterisation (biometric applications) and speech characterisation (non-biometric applications). Glancing at non-biometrics, methods are presented to avoid function creep, preventing the exploitation of biometric information, e.g., to single out an identity in speech-assisted health care via speaker characterisation. In promoting harmonised research, the article also outlines common, empirical evaluation metrics for the assessment of privacy-preserving technologies for speech data.

Comunicació presentada a: SECRYPT 2006, International Conference on Security and Cryptography, celebrada a Setúbal, Portugal, del 7 al 10 d'agost de 2006 ; Prosodic information can be used successfully for automatic speaker recognition, although most of the speaker recognition systems use only short-term spectral features as voice information. In this work, prosody information is added to a multimodal system based on face and voice characteristics in order to improve the performance of the system. Fusion is carried out by using various fusion strategies and two different fusion techniques: support vector machines and matcher weighting. Results are clearly improved when a previous normalization based on histogram equalization is done before the fusion of the monomodal scores. ; This work has been partially supported by the European Union (under CHIL IST-2002-506909 and BIOSEC IST-2002-001766) and by the Spanish Government (under ACESCA project TIN2005-08852 and grant AP2003-3598).

In the light of current lifestyle of humankind, there is a great need of high secure interfaces which apart from providing identification to the user, also enhances security. There are many biometric techniques and various new approaches that are being widely used in the fields of banking sector, security accesses, military, etc. But it is difficult to decide which of them is more feasible and secure. We tried to compare these Biometric Techniques and put forth the pros and cons of each of these methods while keeping a few major parameters as benchmarks. We believe this brief overview would help us to analyze the idea of the above approach, which would promote longevity and enable interoperability.

Each person has individual voice characteristics, which are determined by the characteristics of the structure of his vocal organs. In the process of communication, people are able to discern the voices of other people on a subconscious level, but for computing technology this task is non-trivial and requires focused research.The purpose of the article is to analyze the existing methods of recognition of speech information, to identify their weak and strong points in order to justify the choice of the most receptive regarding the recognition of the speaker by voice.The growth of the global market for voice recognition devices depends on many factors. One of the main factors is the increase in demand for voice biometrics services. With the increasing complexity and frequency of security breaches, the latter continues to be one of the main requirements for the Armed Forces of Ukraine. The high demand for voice biometrics, which is unique to any person, is crucial in determining a person's identity.Military departments in most countries use extremely restricted areas to prevent intruders from entering. To ensure secrecy and security in this area, the military uses voice recognition systems.Any recognition system works in two modes: in the registration mode and the identification mode. In other words, you need to have an example voice.Currently, there are a number of methods that allow solving problems of text-independent speaker identification by voice, and each of these methods has its own advantages and disadvantages. However, the most common method is the Gaussian Mixture Model. Models of Gaussian mixtures have proven themselves as a stochastic model for building recognition systems. They are convenient not only for modeling the characteristics of the speaker's voice, but also for the recording channel and the environment.An effective speech recognition system should include the following steps in processing the input signal: noise removal, segmentation, selection of voiced sections, parameterization, recognition, and correction with a feedback dictionary. ; Каждый человек имеет индивидуальные голосовые характеристики, которые определяются особенностями строения его голосовых органов. В процессе общения люди способны на подсознательном уровне различать голоса других людей, однако для вычислительной техники эта задача является нетривиальной и требует целенаправленных исследований.Цель статьи ‑ анализ существующих методов распознавания речевой информации, определение их слабых и сильных сторон для обоснования выбора наиболее восприимчивого относительно распознавания диктора по голосу.Рост мирового рынка устройств распознавания голоса зависит от множества факторов. Одним из основных факторов является увеличение спроса на услуги голосовой биометрии. С увеличением сложности и частоты нарушений безопасности, последняя продолжает оставаться одним из основных требований для Вооруженных Сил Украины. Высокий спрос голосовой биометрии, которая является уникальной для любого человека, имеет решающее значение в установлении личности человека.Военные ведомства в большинстве стран используют крайне ограниченные зоны для того, чтобы предотвратить проникновение злоумышленников. Для обеспечения секретности и безопасности в этой зоне, военные используют системы распознавания голоса.Любая система распознавания работает в двух режимах: в режиме регистрации и режиме идентификации. Другими словами, необходимо иметь пример голоса.В настоящее время существует определенное количество методов, позволяющих решать задачи текстонезависимой идентификации диктора по голосу, причем каждый из указанных методов имеет свои преимущества и недостатки. Однако, наиболее распространенным методом является Gaussian Mixture Model. Модели гауссовых смесей хорошо себя зарекомендовали в качестве стохастической модели для построения систем распознавания. Они удобны не только для моделирования характеристик голоса диктора, но и канала звукозаписи, окружающей среды.Эффективная система распознавания речи должна предусматривать следующие этапы обработки входного сигнала: удаление шума, сегментация, выделение вокализованных участков, параметризация, распознавание, корректировка по словарю с обратной связью. ; Кожна людина має індивідуальні голосові характеристики, які визначаються особливостями будови його голосових органів. У процесі спілкування люди здатні на підсвідомому рівні розрізняти голоси інших людей, однак для обчислювальної техніки ця задача є нетривіальною і вимагає цілеспрямованих досліджень.Мета статті ‑ аналіз існуючих методів розпізнавання мовної інформації, визначення їх слабких і сильних сторін для обгрунтування вибору найбільш сприйнятливого стосовно розпізнавання диктора за голосом.Зростання світового ринку пристроїв розпізнавання голосу залежить від множини факторів. Одним з основних факторів є збільшення попиту на послуги голосової біометрії. Зі збільшенням складності і частоти порушень безпеки, остання продовжує залишатися одним з основних вимог для Збройних Сил України. Високий попит голосової біометрії, яка є унікальною для будь-якої людини, має вирішальне значення у встановленні особи людини.Військові відомства в більшості країн використовують вкрай обмежені зони для того, щоб запобігти проникненню зловмисників. Для забезпечення секретності і безпеки в цій зоні, військові використовують системи розпізнавання голосу.Будь-яка система розпізнавання працює в двох режимах: в режимі реєстрації та режимі ідентифікації. Іншими словами, необхідно мати приклад голосу.На даний час існує певна кількість методів, що дають змогу вирішувати завдання текстонезалежної ідентифікації диктора за голосом, причому кожен із наведених методів має свої переваги та недоліки. Проте, найбільш поширеним методом є Gaussian Mixture Model. Моделі гаусових сумішей добре себе зарекомендували в якості стохастичної моделі для побудови систем розпізнавання. Вони зручні не тільки для моделювання характеристик голосу диктора, але і каналу звукозапису, навколишнього середовища.Ефективна система розпізнавання мови має враховувати такі етапи обробки вхідного сигналу, як видалення шуму, сегментація, виділення вокалізованих ділянок, параметризація, розпізнавання, коригування за словником з оберненим зв'язком.