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 / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

51 Citations (Scopus)


2014 ACM. 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.
Original languageEnglish
Title of host publicationICMI 2014 - Proceedings of the 2014 International Conference on Multimodal Interaction
PublisherAssociation for Computing Machinery, Inc
Number of pages6
ISBN (Electronic)9781450328852
Publication statusPublished - 12 Nov 2014
Event16th ACM International Conference on Multimodal Interaction, ICMI 2014 - Istanbul, Turkey
Duration: 12 Nov 201416 Nov 2014


Conference16th ACM International Conference on Multimodal Interaction, ICMI 2014


  • Emotion recognition
  • Feature fusion
  • Multiple kernel learning
  • Support vector machine

ASJC Scopus subject areas

  • Human-Computer Interaction

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