02-Biometrics-Lecture2-Day1-2007-11-00-12-30

02-Biometrics-Lecture2-Day1-2007-11-00-12-30 - 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 Identity and Biometrics Identifiable Biometric Characteristics Authentication, Verification and Identification Performance Evaluation Applications A Quick Overview of Biometrics
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3 Speaker Recognition Dynamic Signature Fingerprints Iris Retina Face Others Multimodal Biometrics A Quick Overview of Biometrics
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4 « Open sesame »
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5 Principal structure of speaker recognition systems Recognition result Speech wave Training Recognition Feature extraction Reference templates/models for each speaker Similarity (Distance)
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6 0109-22 Frame Window Feature vector Feature Extraction
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7 Gaussian Mixture Model (GMM) 12 () (1) ( (2) ) T T T vD v vv v v v v D ⎤⎡ ⎥⎢ ••• •• ⎦⎣ Acoustic vectors for training GMM Feature 1 Feature 2 Feature D Histograms score = log-likelihood (speech | model)
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8 Open sesame ! Enrollment session Enrollment session Test session Test session GMM training GMM training Speaker verifier Speaker verifier Training utterances: “Open sesame” “Open sesame” “Open sesame” Scores Test utterance: “Open sesame” Identity claim Database Database Speaker-Dependent GMM
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9 Speaker Recognition Dynamic Signature Fingerprints Iris Retina Face Others Multimodal Biometrics A Quick Overview of Biometrics
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10 Signature Static off-line technology – document authentication Dynamic on-line technology – signal processing and pattern recognition
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11 Dynamic Signature Features: 1. coordinate X 2. coordinate Y 3. pressure 4. pen azimuth (0° - 359°) 5. pen altitude (0º - 90°) Altitude Azimuth
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12 Segment models Feature vectors Segment k -1 Segment k Segment k +1 time 1 1 3 3 2 2 Output probabilities b 1 ( x ) b 2 ( x ) b 3 ( x ) xx x 0.2 0.4 0.7 0.5 0.6 0.3 0.3 Hidden Markov Model (HMM)
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13 Super PDA The Delivery Information Acquisition Device -that Brown tablet you sign when receiving a package - is one hell of a PDA. The fourth version can handle personal (Bluetooth), local (802.11b), and wide (GPRS or CDMA) area wireless networking. It also has a dialup modem, an infrared scanner, GPS capability, 128 Mbytes of RAM, and a color screen. All UPS drivers carry the device for capturing and transmitting parcel pickup and delivery data.
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14 Speaker Recognition Dynamic Signature Fingerprints Iris Retina Face Others Multimodal Biometrics A Quick Overview of Biometrics
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02-Biometrics-Lecture2-Day1-2007-11-00-12-30 - Continuing...

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