Emotion Recognition in the Wild with Feature Fusion and Multiple Kernel Learning

JunKai Chen · Zenghai Chen · Zheru Chi · Hong Chuan Fu

105 citations0/0 files storedINTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION

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Emotion and Mood Recognition Face recognition and analysis Face and Expression Recognition

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.

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References

2005Web-Based Database for Facial Expression Analysis 2002Facial and Vocal Expressions of Emotion 2012Collecting Large, Richly Annotated Facial-Expression Databases from Movies 2011Static facial expression analysis in tough conditions: Data, evaluation protocol and benchmark 2009OpenEAR — Introducing the munich open-source emotion and affect recognition toolkit 2013Deep learning for robust feature generation in audiovisual emotion recognition 2013Combining modality specific deep neural networks for emotion recognition in video 2011AVEC 2011–The First International Audio/Visual Emotion Challenge

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2016Sparse Kernel Reduced-Rank Regression for Bimodal Emotion Recognition From Facial Expression and Speech 2020Automatic Detection of Pain from Facial Expressions: A Survey 2021End-to-end multimodal clinical depression recognition using deep neural networks: A comparative analysis 2016Multi-modal Conditional Attention Fusion for Dimensional Emotion Prediction 2020Visual-audio emotion recognition based on multi-task and ensemble learning with multiple features

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