Work2022

Face2Exp: Combating Data Biases for Facial Expression Recognition

Dan Zeng · Zhiyuan Lin · Yan Xiao · Yuting Liu · Fei Wang

141 citations0/0 files storedIEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings

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Emotion and Mood Recognition Face recognition and analysis Domain Adaptation and Few-Shot Learning

Abstract

Facial expression recognition (FER) is challenging due to the class imbalance caused by data collection. Existing studies tackle the data bias problem using only labeled facial expression dataset. Orthogonal to existing FER methods, we propose to utilize large unlabeled face recognition (FR) datasets to enhance FER. However, this raises another data bias problem—the distribution mismatch between FR and FER data. To combat the mismatch, we propose the Meta-Face2Exp framework, which consists of a base network and an adaptation network. The base network learns prior expression knowledge on class-balanced FER data while the adaptation network is trained to fit the pseudo labels of FR data generated by the base model. To combat the mismatch between FR and FER data, Meta-Face2Exp uses a circuit feedback mechanism, which improves the base network with the feedback from the adaptation network. Experiments show that our MetaFace2Exp achieves comparable accuracy to state-of-the-art FER methods with 10% of the labeled FER data utilized by the baselines. We also demonstrate that the circuit feedback mechanism successfully eliminates data bias11Code is available at link: https://github.com/danzeng1990/Face2Exp..

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References

2017AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild 2017Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild 2020Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition 2018Occlusion Aware Facial Expression Recognition Using CNN With Attention Mechanism 2020Suppressing Uncertainties for Large-Scale Facial Expression Recognition 2003Real Time Face Detection and Facial Expression Recognition: Development and Applications to Human Computer Interaction.2017FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition 2018Facial Expression Recognition with Inconsistently Annotated Datasets

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2024MMATrans: Muscle Movement Aware Representation Learning for Facial Expression Recognition via Transformers 2023Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion 2023FG-AGR: Fine-Grained Associative Graph Representation for Facial Expression Recognition in the Wild 2024CLIPER: A Unified Vision-Language Framework for In-the-Wild Facial Expression Recognition

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