05-Biometrics-Lecture1-Day2-09-00-10-30

05-Biometrics-Lecture1-Day2-09-00-10-30 - Continuing...

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1 Biometric Identity Verification http://scgwww.epfl.ch/courses Continuing Education – COST 2101 Training School Dr. Andrzej Drygajlo Speech Processing and Biometrics Group Signal Processing Institute Ecole Polytechnique Fédérale de Lausanne (EPFL) Center for Interdisciplinary Studies in Information Security (ISIS)
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2 Biometrics - Contents Fundamentals of Biometrics Analysis, Modeling and Interpretation of Biometric Data Leading Biometric Technology Multimodal Biometrics Biometric Standards Integration of biometrics with other existing technologies Cryptography and Biometric Data Privacy and Legal Issues
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3 Biometrics - Contents Fundamentals of Biometrics Identity and Biometrics Individuality of Biometric Data Recognition, Verification, Identification and Authentication Analysis, Modeling and Interpretation of Biometric Data Mathematical Tools Sensing and Storage Representation and Feature Extraction Enrollment and Template Creation Biometric System Errors Evaluation of Biometric Systems
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4 Face Speaker Recognition Dynamic Signature Fingerprints Iris Hand Others Leading Biometric Technology
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5 Popular biometric characteristics (modalities) Fingerprint Voice Signature Face Iris Hand
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6 "Open Sesame!" Ali Baba and the fourty thieves
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7 Extracting Information from Speech
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8 Comparison of biometric techniques ACCURACY COST Fingerprint Vo ice / Speech Signature Fa ce Hand Iris
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9 Speaker Recognition Generalities Feature extraction Speaker models and templates Speaker recognition errors Speaker recognition systems Advantages and disadvantages of voice as biometric
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10 Speaker Recognition Applications
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11 Evolution of Speaker Recognition
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12 Speaker Recognition Tasks Speaker recognition – general term used to include all of the many different tasks of discriminating people based on the sound of their voices, e.g. speaker identification, speaker verification, speaker segmentation and clustering. Speaker identification – task of deciding, given a sample of speech, who among many candidate speakers said it. This is an N-class decision task, where N is the number of candidate speakers. Speaker verification – task of deciding, given a sample of speech, whether a specified candidate speaker said it. This is a 2-class task. Authentication – (identity verification) is a process used to link a physical person with a chosen identity
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13 Speaker Recognition Tasks
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14 Speaker Identification ••• Similarity Similarity Similarity Similarity Similarity Similarity Reference template or model (Speaker #N) Reference template or model (Speaker #N) Reference template or model (Speaker #2) Reference template or model (Speaker #2) Reference template or model (Speaker #1) Reference template or model (Speaker #1) Feature extraction Feature extraction Maximum selection Maximum selection Speech wave Identification result (Speaker ID)
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15 Front-end Model database Speaker identification Speaker Identity Speech wave Score computation Best-match
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This note was uploaded on 06/25/2009 for the course MATH MAT 400 taught by Professor Jamespotvein during the Fall '08 term at University of Toronto- Toronto.

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05-Biometrics-Lecture1-Day2-09-00-10-30 - Continuing...

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