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cs.CV2024

Spatial Action Unit Cues for Interpretable Deep Facial Expression Recognition

Soufiane Belharbi, Marco Pedersoli, Alessandro Lameiras Koerich +2

Although state-of-the-art classifiers for facial expression recognition (FER) can achieve a high level of accuracy, they lack interpretability, an important feature for end-users.…

cs.CV2024

Textualized and Feature-based Models for Compound Multimodal Emotion Recognition in the Wild

Nicolas Richet, Soufiane Belharbi, Haseeb Aslam +8

Systems for multimodal emotion recognition (ER) are commonly trained to extract features from different modalities (e.g., visual, audio, and textual) that are combined to predict i…

cs.CV2024

Joint Multimodal Transformer for Emotion Recognition in the Wild

Paul Waligora, Haseeb Aslam, Osama Zeeshan +5

Multimodal emotion recognition (MMER) systems typically outperform unimodal systems by leveraging the inter- and intra-modal relationships between, e.g., visual, textual, physiolog…

cs.CV2024

Guided Interpretable Facial Expression Recognition via Spatial Action Unit Cues

Soufiane Belharbi, Marco Pedersoli, Alessandro Lameiras Koerich +2

Although state-of-the-art classifiers for facial expression recognition (FER) can achieve a high level of accuracy, they lack interpretability, an important feature for end-users.…

cs.CV2024

Distilling Privileged Multimodal Information for Expression Recognition using Optimal Transport

Muhammad Haseeb Aslam, Muhammad Osama Zeeshan, Soufiane Belharbi +4

Deep learning models for multimodal expression recognition have reached remarkable performance in controlled laboratory environments because of their ability to learn complementary…