Multiple kernel learning for emotion recognition in the wild | Zendy

Karan Sikka | Zendy; Karmen Dykstra | Zendy; Suchitra Sathyanarayana | Zendy; Gwen Littlewort | Zendy; Marian Stewart Bartlett | Zendy

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Multiple kernel learning for emotion recognition in the wild

Author(s) -

Karan Sikka,

Karmen Dykstra,

Suchitra Sathyanarayana,

Gwen Littlewort,

Marian Stewart Bartlett

Publication year - 2013

Publication title -

citeseer x (the pennsylvania state university)

Language(s) - English

Resource type - Conference proceedings

DOI - 10.1145/2522848.2531741

Subject(s) - sadness , paralanguage , disgust , computer science , surprise , artificial intelligence , support vector machine , happiness , emotion classification , anger , speech recognition , kernel (algebra) , clips , pattern recognition (psychology) , machine learning , psychology , communication , mathematics , social psychology , combinatorics , psychiatry

We propose a method to automatically detect emotions in unconstrained settings as part of the 2013 Emotion Recognition in the Wild Challenge [16], organized in conjunction with the ACM International Conference on Multimodal Interaction (ICMI 2013). Our method combines multiple visual descriptors with paralinguistic audio features for multimodal classification of video clips. Extracted features are combined using Multiple Kernel Learning and the clips are classified using an SVM into one of the seven emotion categories: Anger, Disgust, Fear, Happiness, Neutral, Sadness and Surprise. The proposed method achieves competitive results, with an accuracy gain of approximately 10% above the challenge baseline.

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