Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/55507
Title: Emotion recognition in the wild with feature fusion and multiple kernel learning
Authors: Chen, J
Chen, Z
Chi, Z 
Fu, H
Keywords: Emotion recognition
Feature fusion
HOG-TOP
Multiple kernel learning
Support vector machine
Issue Date: 2014
Publisher: ACM
Source: ICMI'14 : proceedings of the 2014 International Conference on Multimodal Interaction, November 12-16, 2014, Istanbul, Turkey, p. 508-513. New York, NY: ACM, 2014 How to cite?
Abstract: This paper presents our proposed approach for the second Emotion Recognition in The Wild Challenge. We propose a new feature descriptor called Histogram of Oriented Gradients from Three Orthogonal Planes (HOG-TOP) to represent facial expressions. We also explore the properties of visual features and audio features, and adopt Multiple Kernel Learning (MKL) to find an optimal feature fusion. An SVM with multiple kernels is trained for the facial expression classification. Experimental results demonstrate that our method achieves a promising performance. The overall classification accuracy on the validation set and test set are 40.21% and 45.21%, respectively
URI: http://hdl.handle.net/10397/55507
ISBN: 9781450328852
DOI: 10.1145/2663204.2666277
Appears in Collections:Conference Paper

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