Hierarchical Committee of Deep CNNs with Exponentially-Weighted Decision Fusion for Static Facial Expression Recognition

Bo-Kyeong Kim · Hwaran Lee · Jihyeon Roh · Soo-Young Lee

126 citations0/0 files storedACM International Conference on Multimodal Interaction (ICMI)

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

Abstract

We present a pattern recognition framework to improve committee machines of deep convolutional neural networks (deep CNNs) and its application to static facial expression recognition in the wild (SFEW). In order to generate enough diversity of decisions, we trained multiple deep CNNs by varying network architectures, input normalization, and weight initialization as well as by adopting several learning strategies to use large external databases. Moreover, with these deep models, we formed hierarchical committees using the validation-accuracy-based exponentially-weighted average (VA-Expo-WA) rule. Through extensive experiments, the great strengths of our committee machines were demonstrated in both structural and decisional ways. On the SFEW2.0 dataset released for the 3rd Emotion Recognition in the Wild (EmotiW) sub-challenge, a test accuracy of 57.3% was obtained from the best single deep CNN, while the single-level committees yielded 58.3% and 60.5% with the simple average rule and with the VA-Expo-WA rule, respectively. Our final submission based on the 3-level hierarchy using the VA-Expo-WA achieved 61.6%, significantly higher than the SFEW baseline of 39.1%.

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References

2012Collecting Large, Richly Annotated Facial-Expression Databases from Movies 2013Combining modality specific deep neural networks for emotion recognition in video 2015Video and Image based Emotion Recognition Challenges in the Wild 2010Emotion Recognition of Affective Speech Based on Multiple Classifiers Using Acoustic-Prosodic Information and Semantic Labels

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2018Video facial emotion recognition based on local enhanced motion history image and CNN-CTSLSTM networks 2017Supervised Committee of Convolutional Neural Networks in Automated Facial Expression Analysis 2018A visual attention based ROI detection method for facial expression recognition 2016Baseline CNN structure analysis for facial expression recognition 2021Co-attentive multi-task convolutional neural network for facial expression recognition 2017Group-level emotion recognition using transfer learning from face identification 2017Multi-modal Score Fusion and Decision Trees for Explainable Automatic Job Candidate Screening from Video CVs 2016Combining Deep Facial and Ambient Features for First Impression Estimation

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