Emotion recognition in the wild with feature fusion and multiple kernel learning

Junkai CHEN, Zenghai CHEN, Zheru CHI, Hong FU

Research output: Chapter in Book/Report/Conference proceedingChapters

49 Citations (Scopus)

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. Copyright © 2014 ACM.
Original languageEnglish
Title of host publicationICMI '14: Proceedings of the 16th International Conference on Multimodal Interaction
Place of PublicationNew York
PublisherAssociation for Computing Machinery
Pages508-513
ISBN (Print)9781450328852, 1450328857
DOIs
Publication statusPublished - Nov 2014

Citation

Chen, J., Chen, Z., Chi, Z., & Fu, H. (2014). Emotion recognition in the wild with feature fusion and multiple kernel learning. In ICMI '14: Proceedings of the 16th International Conference on Multimodal Interaction (pp. 508-513). New York: Association for Computing Machinery.

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