Mixture of SVMs for Face Class Modeling
author:
Julien Meynet,
Signal Processing Institute
Description
We present a method for face detection which uses a new {SVM} structure trained in an expert manner in the eigenface space. This robust method has been introduced as a post processing step in a real-time face detection system. The principle is to train several parallel {SVMs} on subsets of some initial training set and then train a second layer {SVM} on the margins of the first layer of {SVMa}. This approach presents a number of advantages over the classical {SVM}: firstly the training time is considerably reduced and secondly the classification performance is improved, we will present some comparisions with the single {SVM} approach for the case of human face class modeling.
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| Slides | |
| 0:01 | Mixture of SVMs for Face Class Modeling |
| 0:15 | Outline |
| 0:58 | Face Detection Methods |
| 1:59 | Principle of the detection |
| 2:49 | Eigenfaces Space |
| 3:46 | Distance From Feature Space |
| 4:25 | Support Vector Machines (SVM) |
| 5:44 | Mixture of SVMs (MSVM) |
| 7:08 | Mixture of SVMs |
| 8:19 | Mixture of SVMs |
| 8:59 | Experiments and Results I |
| 10:14 | Experiments and Results II |
| 11:36 | Experiments and Results III |
| 13:04 | Conclusions - Future Work |
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Good presentation. I appreciate it.