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    Author: Sarkar, Sudeep
    Sponsored by:IEEE Educational Activities and the IEEE Biometrics Council
    Tutorial Level: Introductory
    Publication Date: Oct-2010
    Run Time: 1:00:00
    CEUs: .3
    PDHs: 3
    ECSA CPD (Category 1 - Development Activities): 1 - Includes study time

    Abstract
    It has been folklore that humans can identify others based on their biological movement from a distance. This observation was somewhat bolstered by experiments with light point displays by human perception researchers in the 70s and have been confirmed by recent human perception experiments. However, it is only recently that computer vision based gait biometrics has received much attention. Recent research on this topic, much of it facilitated by the structure of the DARPA HumanID Gait Challenge Problem, has brought into light interesting capabilities and limits of this modality. Recognition is possible from gait.

    The tutorial will start by describing how this challenge framework, consisting of data sets, challenge experiments, and a baseline performance, has helped jump start the gait recognition area. It will also summarize some of the lessons learned in terms of what are the sources of gait variations that are easy to overcome and what are still the outstanding ones. Perhaps from a vision point of view one of the important observations that some researchers have made is that gait shapes offer more stable cues for recognition, across different covariates, than gait dynamics. Building on these observations, we will summarize an approach that first performs gait dynamics normalization using population HMM and then computes distances between gait shapes in a space that maximizes differences between individuals. This algorithm statistically improves recognition over all covariates in the DARPA HumanID Gait Challenge Problem.

    Other possible biometrics that can be captured at a distance is face and voice, i.e. automatic speaker recognition, which is a mature research area. Current challenges lie in the area of voice recognition at remote distances using readily available remote microphones or microphone arrays. Other research avenues include making the speaker recognition system robust to background noise and microphone type. The tutorial will end by presenting some ideas from biometric fusion to improve recognition at a distance.

    Keywords: biometrics , computer vision , face recognition , gait biometrics , gait recognition , recognition , recognition at a distance , signal processing , voice recognition

    For individuals not subscribed to the IEEE eLearning Library, this course is available for individual purchase.

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    Author:Plataniotis, Kostas
    Sponsored by:IEEE Educational Activities Department
    Tutorial Level: Intermediate
    Publication Date: Oct-2008
    Run Time: 1:00:00
    CEUs: .3
    PDHs: 3
    ECSA CPD (Category 1 - Development Activities): 1 - Includes study time


    Abstract
    This course will provide an overview of the study of methods for uniquely recognizing humans based upon one or more intrinsic physical or behavioral traits. The fundamentals of biometrics and biometric systems will be presented. The course will delve into why biometrics is a solution for security and authentication. Face, gait and ECG based biometrics will be covered. Biometrics and encryption will also be discussed, and the course will conclude with a discussion of future steps.

    Keywords: Biometric System , Biometrics , ECG , EER; Face Recognition , Electro-cardiogram , Encryption , Equal Error Rate , FAR , FRR , False Acceptance Rate

    For individuals not subscribed to the IEEE eLearning Library, this course is available for individual purchase.

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    Author: Boult, Terrance and Scheirer, Walter
    Sponsored by:IEEE Educational Activities and the IEEE Biometrics Council
    Tutorial Level: Introductory
    Publication Date: Dec-2010
    Run Time: 1:00:00
    CEUs: .3
    PDHs: 3
    ECSA CPD (Category 1 - Development Activities): 1 - Includes study time

    Abstract
    This tutorial will cover the privacy and security of biometrics. The first part, will address the concept of identity and its ethical implications. The concept of personal identity is important from several perspectives. From a cultural perspective, the more the world converges, the more individual cultures wish to maintain their separate identities. From an individual perspective, the greater the population and the tendency to reduce people to stereotypes, the greater the desire to establish an individual identity. There is, however, another level where identity and the verification of identity, is becoming increasingly important in relation to all manner of transactions, from those related to mobility, to those related to legal, and political, rights and obligations, and finally to financial and economical transactions. The intrusion of technology into these areas is not new, but their heightened visibility and ubiquity can create anxiety. This holds particularly true for biometrics.

    This tutorial will present the security and privacy issues with traditional biometrics; introduce the Biometrics Dilemma and the various threats it poses; and describe a model for biometric DB risk, highlighting the problem with standard large-scale biometrics. The tutorial will explain why standard encryption does not solve the key problems, but also explores best practices in using standard encryption, which can improve security. Moving to security, the tutorial will examine security system architectures, the role of authentication in such systems and the standard architectures for authentication using biometrics.

    An examination of the advantages that biometrics bring, how biometrics can improve security and even privacy in such systems, and a discussion of their weakness in both security and privacy will be presented. The tutorial will briefly discusses the Nobel Prize winning Economic theory of asymmetric information, Akerlof's market for lemons and Kerckhoffs' principles for security, and their implications for biometrics systems, especially large scale deployments. The last component of the tutorial is an in-depth review of the state of the art in what is sometimes called biometric template protection, including biometric encryption, fuzzy vaults, fuzzy extractors, biometric hashing, and cancelable biometrics. The tutorial will walk through a security analysis of these technologies including the published attacks.

    Keywords: Privacy , biometrics

    For individuals not subscribed to the IEEE eLearning Library, this course is available for individual purchase.

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    Author: Boult, Terrance and Scheirer, Walter
    Sponsored by:IEEE Educational Activities and the IEEE Biometrics Council
    Tutorial Level: Introductory
    Publication Date: Dec-2010
    Run Time: 1:00:00
    CEUs: .3
    PDHs: 3
    ECSA CPD (Category 1 - Development Activities): 1 - Includes study time

    Abstract
    This tutorial introduces issues in long-range facial image acquisition and measures for image quality and their usage, as well as subsequent challenges for face recognition. The first several modules on image acquisition for face recognition discuss concerns related to lighting, sensors, and lenses, which impact short-range biometrics, but are more pronounced in long-range biometrics. We then go on to introduce the design of controlled experiments for long-range face recognition and why they are needed. With our experiments, we go on to show some of the weather and atmospheric effects that occur for long-range imaging, with numerous examples. Next, we address measurements of “system quality,” including image-quality measures and their use in the prediction of face recognition algorithm performance. That module introduces the concept of post-recognition score analysis and techniques for analyzing different “quality” measures. The last two modules of this tutorial explore long-range face recognition directly. Facial feature detection is an important prerequisite for face recognition, and we look at two different approaches for accurately accomplishing this for long-range scenarios. Finally, we address the very difficult problem of blur – both motion and atmospheric – including common sources in acquisition and algorithms to mitigate its effects.

    Keywords: biometrics , security , surveillance

    For individuals not subscribed to the IEEE eLearning Library, this course is available for individual purchase.

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    Author: Franco, Annalisa
    Sponsored by:IEEE Educational Activities and the IEEE Biometrics Council
    Tutorial Level: Introductory
    Publication Date:Jan -2011
    Run Time: 1:00:00
    CEUs: .3
    PDHs: 3
    ECSA CPD (Category 1 - Development Activities): 1 - Includes study time

    Abstract
    This tutorial presents fingerprint recognition systems and discusses the main steps of the recognition process. After an overview of image acquisition technologies, we will discuss the different stages of feature extraction, the main approaches to fingerprint matching, and the relevant state-of-the-art algorithms. The tutorial concludes with a review of the major challenges and issues in this field.

    Keywords: biometrics , fingerprint , recognition

    For individuals not subscribed to the IEEE eLearning Library, this course is available for individual purchase.

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    Author: Nixon, Mark S.
    Sponsored by:IEEE Educational Activities and the IEEE Biometrics Council
    Tutorial Level: Introductory
    Publication Date: Dec-2010
    Run Time: 1:00:00
    CEUs: .3
    PDHs: 3
    ECSA CPD (Category 1 - Development Activities): 1 - Includes study time

    Abstract
    In this tutorial, we’re going to cover the background to gait as a biometric. We’re going to consider studies in medicine, psychology, and literature, all of which have evidence that people can be recognized by the way that they walk. We are then going to look at databases that we can use to evaluate gait recognition potential. We will cover the three main databases that are in current use.

    The main techniques will be covered. We’ll look at silhouette based techniques and model based techniques. These are the two main approaches, as they are in many biometrics. Although we have model-based face and holistic face, in gait recognition we have silhouette based and model based.
    We’re then going to consider extensions to these techniques. Among the most important is viewpoint invariance. We will look at normalizing the signatures to be invariant to the relative viewpoint of the camera to the subject. We are then going to see how different shoes and different clothes affect the way you walk. In addition, we will look at a new database and a new system.
    We are going to cover the main components in automatic gait recognition, the main techniques, and the requirements of this new technology. We will use many sources of information, and so there are apologies if your own is missed, or perhaps your favorite technique is not covered. The references will either be on the page in which they are used, or the main ones will be collected together at the end of this talk. The aim is to show you that gait is a large and rapidly advancing field of research.

    Keywords: biometrics , gait , posture

    For individuals not subscribed to the IEEE eLearning Library, this course is available for individual purchase.

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    Author: Beigi, Homayoon
    Sponsored by:IEEE Educational Activities Department and IEEE Biometrics Council
    Tutorial Level: Introductory
    Publication Date: Sept-2010
    Run Time: 1:00:00
    CEUs: .3
    PDHs: 3
    ECSA CPD (Category 1 - Development Activities): 1 - Includes study time

    Abstract

    This tutorial provides an in-depth look at speaker recognition. Speaker recognition is a technique that uses the vocal characteristics of an individual's voice to be able to identify the person and verify the person. Different forms and modalities of speaker recognition will be discussed in this tutorial. Regardless of the forms and modalities of speaker recognition, ultimately, speaker recognition tries to identify the person based on their vocal characteristics. It is often confused with speech recognition, which has been around for a long time. Speech recognition is the act of transcribing what's being said, whereas speaker recognition really does not have much to do with transcription and is more concerned with recognizing the individual who is uttering the speech.

    Keywords: Allophone , Articulation , Coordination , Initiation , Phonation , Phone , Phoneme

    For individuals not subscribed to the IEEE eLearning Library, this course is available for individual purchase.