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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Kumar, V.P. Poggio, T. |
| Copyright Year | 2000 |
| Description | Author affiliation: Dept. of Brain & Cognitive Sci., MIT, Cambridge, MA, USA (Kumar, V.P.) |
| Abstract | This paper describes a trainable system capable of tracking faces and facial features like eyes and nostrils and estimating basic mouth features such as degrees 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 an 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. |
| Starting Page | 96 |
| Ending Page | 101 |
| File Size | 232727 |
| Page Count | 6 |
| File Format | |
| ISBN | 0769505805 |
| DOI | 10.1109/AFGR.2000.840618 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2000-03-30 |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Facial features Mouth Face detection Image representation Robustness Parameter estimation Support vector machines Support vector machine classification Eyes Real time systems |
| Content Type | Text |
| Resource Type | Article |
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