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Gaze-enhanced Crossmodal Embeddings for Emotion Recognition

Ahmed Abdou, Ekta Sood, Philipp Müller, Andreas Bulling

Proc. International Symposium on Eye Tracking Research and Applications (ETRA), pp. , 2022.


Abstract

Emotional expressions are inherently multimodal – integrating facial behavior, speech, and gaze – but their automatic recognition is often limited to a single modality, e.g. speech during a phone call. While previous work proposed crossmodal emotion embeddings to improve monomodal recognition performance, despite its importance, a representation of gaze was not included. We propose a new approach to emotion recognition that incorporates an explicit representation of gaze in a crossmodal emotion embedding framework. We show that our method outperforms the previous state of the art for both audio-only and video-only emotion classification on the popular One-Minute Gradual Emotion Recognition dataset. Furthermore, we report extensive ablation experiments and provide insights into the performance of different state-of-the-art gaze representations and integration strategies. Our results not only underline the importance of gaze for emotion recognition but also demonstrate a practical and highly effective approach to leveraging gaze information for this task.

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BibTeX

@inproceedings{abdou22_etra, title = {Gaze-enhanced Crossmodal Embeddings for Emotion Recognition}, author = {Abdou, Ahmed and Sood, Ekta and Müller, Philipp and Bulling, Andreas}, year = {2022}, booktitle = {Proc. International Symposium on Eye Tracking Research and Applications (ETRA)}, doi = {}, pages = {} }