@inproceedings{luna-jimenez-etal-2026-emotion,
title = "Emotion Recognition in {G}erman {S}ign {L}anguage with Facial Action Units",
author = "Luna Jimenez, Cristina and
Eing, Lennart and
Esteban Romero, Sergio and
Schneeberger, Tanja and
Gebhard, Patrick and
Nunnari, Fabrizio and
Andre, Elisabeth",
editor = "Efthimiou, Eleni and
Fotinea, Stavroula-Evita and
Hanke, Thomas and
Hochgesang, Julie A. and
Mesch, Johanna and
Schulder, Marc",
booktitle = "Proceedings of the {LREC} 2026 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.signlang-1.31/",
doi = "10.63317/37cwjrcccu7p",
pages = "297--305",
abstract = "Emotion Recognition research in Sign Languages is still in its infancy. Still today, there exists a lack of knowledge about appropriate annotation guidelines and the impact that facial expressions, body postures and head positions have in recognizing emotions while signing, considering that sign language encompasses manual and non-manual cues with linguistic purposes. In this article, we present an acquisition protocol to record acted emotions in German Sign Language under four scenarios (High-Valence and High-Arousal, High-Valence and Low Arousal, Low-Valence and High-Arousal, and Low-Valence and Low-Arousal). The goal is to provide a reference dataset to explore the use of machine learning techniques for an automated classification of emotions in sign language utterances. As a baseline reference, we trained static models with features extracted from the facial muscle activations. The best model achieved an accuracy of 68.84{\%} and a F1 of 67.96{\%} with a random forest trained on the statistics extracted from Action Units. These results highlight the importance of facial expression in sign language, not only for carrying linguistic information but also for transmitting emotions. Results also indicate challenges in detecting emotions in the High-Valence and Low Arousal scenario, which suggests future investigation lines to explore."
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<abstract>Emotion Recognition research in Sign Languages is still in its infancy. Still today, there exists a lack of knowledge about appropriate annotation guidelines and the impact that facial expressions, body postures and head positions have in recognizing emotions while signing, considering that sign language encompasses manual and non-manual cues with linguistic purposes. In this article, we present an acquisition protocol to record acted emotions in German Sign Language under four scenarios (High-Valence and High-Arousal, High-Valence and Low Arousal, Low-Valence and High-Arousal, and Low-Valence and Low-Arousal). The goal is to provide a reference dataset to explore the use of machine learning techniques for an automated classification of emotions in sign language utterances. As a baseline reference, we trained static models with features extracted from the facial muscle activations. The best model achieved an accuracy of 68.84% and a F1 of 67.96% with a random forest trained on the statistics extracted from Action Units. These results highlight the importance of facial expression in sign language, not only for carrying linguistic information but also for transmitting emotions. Results also indicate challenges in detecting emotions in the High-Valence and Low Arousal scenario, which suggests future investigation lines to explore.</abstract>
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%0 Conference Proceedings
%T Emotion Recognition in German Sign Language with Facial Action Units
%A Luna Jimenez, Cristina
%A Eing, Lennart
%A Esteban Romero, Sergio
%A Schneeberger, Tanja
%A Gebhard, Patrick
%A Nunnari, Fabrizio
%A Andre, Elisabeth
%Y Efthimiou, Eleni
%Y Fotinea, Stavroula-Evita
%Y Hanke, Thomas
%Y Hochgesang, Julie A.
%Y Mesch, Johanna
%Y Schulder, Marc
%S Proceedings of the LREC 2026 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F luna-jimenez-etal-2026-emotion
%X Emotion Recognition research in Sign Languages is still in its infancy. Still today, there exists a lack of knowledge about appropriate annotation guidelines and the impact that facial expressions, body postures and head positions have in recognizing emotions while signing, considering that sign language encompasses manual and non-manual cues with linguistic purposes. In this article, we present an acquisition protocol to record acted emotions in German Sign Language under four scenarios (High-Valence and High-Arousal, High-Valence and Low Arousal, Low-Valence and High-Arousal, and Low-Valence and Low-Arousal). The goal is to provide a reference dataset to explore the use of machine learning techniques for an automated classification of emotions in sign language utterances. As a baseline reference, we trained static models with features extracted from the facial muscle activations. The best model achieved an accuracy of 68.84% and a F1 of 67.96% with a random forest trained on the statistics extracted from Action Units. These results highlight the importance of facial expression in sign language, not only for carrying linguistic information but also for transmitting emotions. Results also indicate challenges in detecting emotions in the High-Valence and Low Arousal scenario, which suggests future investigation lines to explore.
%R 10.63317/37cwjrcccu7p
%U https://aclanthology.org/2026.signlang-1.31/
%U https://doi.org/10.63317/37cwjrcccu7p
%P 297-305
Markdown (Informal)
[Emotion Recognition in German Sign Language with Facial Action Units](https://aclanthology.org/2026.signlang-1.31/) (Luna Jimenez et al., SignLang 2026)
ACL
- Cristina Luna Jimenez, Lennart Eing, Sergio Esteban Romero, Tanja Schneeberger, Patrick Gebhard, Fabrizio Nunnari, and Elisabeth Andre. 2026. Emotion Recognition in German Sign Language with Facial Action Units. In Proceedings of the LREC 2026 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion, pages 297–305, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).