Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

12-2013

Abstract

In this paper, we propose a method for video-based human emotion recognition. For each video clip, all frames are represented as an image set, which can be modeled as a linear subspace to be embedded in Grassmannian manifold. After feature extraction, Class-specific One-to-Rest Partial Least Squares (PLS) is learned on video and audio data respectively to distinguish each class from the other confusing ones. Finally, an optimal fusion of classifiers learned from both modalities (video and audio) is conducted at decision level. Our method is evaluated on the Emotion Recognition In The Wild Challenge (EmotiW 2013). The experimental results on both validation set and blind test set are presented for comparison. The final accuracy achieved on test set outperforms the baseline by 26%

Keywords

emotion recognition; emotiw 2013 challenge; grassmannian manifolds; partial least squares regression

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces

Research Areas

Data Science and Engineering

Publication

Proceedings of the 15th ACM International Conference on Multimodal Interaction, Sydney, Australia 2013 Dec 9-13

First Page

525

Last Page

530

ISBN

9781450321297

Identifier

10.1145/2522848.2531738

Publisher

ACM

City or Country

New York

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