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Multi-view Image Restoration from Plenoptic Raw Images Read chapter Read first chapter

2015 | OriginalPaper | Chapter

A Robust Learning Framework Using PSM and Ameliorated SVMs for Emotional Recognition

Authors : Jinhui Chen, Yosuke Kitano, Yiting Li, Tetsuya Takiguchi, Yasuo Ariki

Published in: Computer Vision - ACCV 2014 Workshops

Publisher: Springer International Publishing

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Abstract

This paper proposes a novel machine-learning framework for facial-expression recognition, which is capable of processing images fast and accurately even without having to rely on a large-scale dataset. The framework is derived from Support Vector Machines (SVMs) but distinguishes itself in three key technical points. First, the measure of the samples normalization is based on the Perturbed Subspace Method (PSM), which is an effective way to improve the robustness of a training system. Second, the framework adopts SURF (Speeded Up Robust Features) as features, which is more suitable for dealing with real-time situations. Third, we use region attributes to revise incorrectly detected visual features (described by invisible image attributes at segmented regions of the image). Combining these approaches, the efficiency of machine learning can be improved. Experiments show that the proposed approach is capable of reducing the number of samples effectively, resulting in an obvious reduction in training time.

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previous chapter 3D Model Automatic Exploration: Smooth and Intelligent Virtual Camera Control
next chapter Subtle Expression Recognition Using Optical Strain Weighted Features
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Metadata
Title
A Robust Learning Framework Using PSM and Ameliorated SVMs for Emotional Recognition
Authors
Jinhui Chen
Yosuke Kitano
Yiting Li
Tetsuya Takiguchi
Yasuo Ariki
Copyright Year
2015
Book
Computer Vision - ACCV 2014 Workshops

Print ISBN: 978-3-319-16630-8

Electronic ISBN: 978-3-319-16631-5

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-16631-5_46

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