08-Biometrics-Lecture-8-Part2-2008-11-10

08-Biometrics-Lecture-8-Part2-2008-11-10 - Master SC...

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1 Biometrics http://scgwww.epfl.ch/courses Master SC – Information and Communication Security 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 Iris Recognition Generalities Imaging Challenges Face detection Face recognition Advantages and disadvantages For detection , localization and tracking we are interested on what every face has in common (to tell a face from “non-faces”). For recognition we are not interested on what faces have in common but rather what differentiate one face from another .
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3 Face Detection Face detection: Identify and locate human faces in an image regardless of their position, scale, in plane rotation, orientation, pose (out of plane rotation), and illumination W The first step for any automatic face recognition system system Face detection methods : Knowledge based Feature-based approaches Template matching methods Appearance - based methods Representation: How to describe a typical face?
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4 Face Detection Methods Knowledge-based methods Encode human knowledge of what constitutes a typical face (usually, the relationship between facial features) Feature-based approaches Aim to find structural features of a face that exist even when the pose, viewpoint, or lighting conditions vary Template matching methods Several standard patterns stored to describe the face as a whole or the facial features separately Appearance-based methods The models (or templates) are learned from a set of training images which capture the representative variability of facial appearance
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5 Knowledge-based method Top-down approach: Multi-resolution focus-of- attention approach Level 1: (lowest resolution): apply the rule “the center part of the face has 4 cells
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08-Biometrics-Lecture-8-Part2-2008-11-10 - Master SC...

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