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Learning-Based Approach to Real Time Tracking and Analysis of Faces

dc.date.accessioned2004-10-20T20:48:42Z
dc.date.accessioned2018-11-24T10:23:11Z
dc.date.available2004-10-20T20:48:42Z
dc.date.available2018-11-24T10:23:11Z
dc.date.issued1999-09-23en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/7172
dc.identifier.urihttp://repository.aust.edu.ng/xmlui/handle/1721.1/7172
dc.description.abstractThis paper describes a trainable system capable of tracking faces and facialsfeatures like eyes and nostrils and estimating basic mouth features such as sdegrees of openness and smile in real time. In developing this system, we have addressed the twin issues of image representation and algorithms for learning. We have used the invariance properties of image representations based on Haar wavelets to robustly capture various facial features. Similarly, unlike previous approaches this system is entirely trained using examples and does not rely on a priori (hand-crafted) models of facial features based on optical flow or facial musculature. The system works in several stages that begin with face detection, followed by localization of facial features and estimation of mouth parameters. Each of these stages is formulated as a problem in supervised learning from examples. We apply the new and robust technique of support vector machines (SVM) for classification in the stage of skin segmentation, face detection and eye detection. Estimation of mouth parameters is modeled as a regression from a sparse subset of coefficients (basis functions) of an overcomplete dictionary of Haar wavelets.en_US
dc.format.extent11 p.en_US
dc.format.extent2942036 bytes
dc.format.extent601056 bytes
dc.language.isoen_US
dc.subjectAIen_US
dc.subjectMITen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectReal Timeen_US
dc.subjectFacesen_US
dc.subjectExpressionsen_US
dc.titleLearning-Based Approach to Real Time Tracking and Analysis of Facesen_US


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