@inproceedings{susman-etal-2026-comparing,
title = "Comparing Computer Vision Instruments for Eye Blink Analysis",
author = "Susman, Margaux and
Miquel Blasco, Carla and
Bulla, Jan",
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-1.47/",
doi = "10.63317/2vzsgmq46vrk",
pages = "459--467",
abstract = "We compared four tools for analyzing blink velocity and amplitude, examining how MediaPipe, OpenFace, InsightFace, and 3DDFA compare in terms of blink analysis. Building on previous findings that different tools yield different results (Kuznetsova and Kimmelman, 2024), we explored their fixed-effect estimates across linguistic versus non-linguistic blinks, within non-linguistic blinks (eye watering blinks versus gaze-direction-change blinks), and within linguistic blinks (prosodic/turn-taking blinks, sign-aligned/list-marking blinks and backchanneling blinks), while controlling for head pose (Pitch, Roll, Yaw). Using mixed-effects linear models on annotated French Sign Language data, we found tool-specific patterns: consistent negative effects for InsightFace and MediaPipe, but positive. effects for 3DDFA. In addition, the influence of head pose varied across models (Pitch is strongly positive in MediaPipe but negative in InsightFace and some 3DDFA models; Roll and Yaw also switch importance across tools). These discrepancies highlight methodological biases that can distort linguistic interpretations."
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<abstract>We compared four tools for analyzing blink velocity and amplitude, examining how MediaPipe, OpenFace, InsightFace, and 3DDFA compare in terms of blink analysis. Building on previous findings that different tools yield different results (Kuznetsova and Kimmelman, 2024), we explored their fixed-effect estimates across linguistic versus non-linguistic blinks, within non-linguistic blinks (eye watering blinks versus gaze-direction-change blinks), and within linguistic blinks (prosodic/turn-taking blinks, sign-aligned/list-marking blinks and backchanneling blinks), while controlling for head pose (Pitch, Roll, Yaw). Using mixed-effects linear models on annotated French Sign Language data, we found tool-specific patterns: consistent negative effects for InsightFace and MediaPipe, but positive. effects for 3DDFA. In addition, the influence of head pose varied across models (Pitch is strongly positive in MediaPipe but negative in InsightFace and some 3DDFA models; Roll and Yaw also switch importance across tools). These discrepancies highlight methodological biases that can distort linguistic interpretations.</abstract>
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%0 Conference Proceedings
%T Comparing Computer Vision Instruments for Eye Blink Analysis
%A Susman, Margaux
%A Miquel Blasco, Carla
%A Bulla, Jan
%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 susman-etal-2026-comparing
%X We compared four tools for analyzing blink velocity and amplitude, examining how MediaPipe, OpenFace, InsightFace, and 3DDFA compare in terms of blink analysis. Building on previous findings that different tools yield different results (Kuznetsova and Kimmelman, 2024), we explored their fixed-effect estimates across linguistic versus non-linguistic blinks, within non-linguistic blinks (eye watering blinks versus gaze-direction-change blinks), and within linguistic blinks (prosodic/turn-taking blinks, sign-aligned/list-marking blinks and backchanneling blinks), while controlling for head pose (Pitch, Roll, Yaw). Using mixed-effects linear models on annotated French Sign Language data, we found tool-specific patterns: consistent negative effects for InsightFace and MediaPipe, but positive. effects for 3DDFA. In addition, the influence of head pose varied across models (Pitch is strongly positive in MediaPipe but negative in InsightFace and some 3DDFA models; Roll and Yaw also switch importance across tools). These discrepancies highlight methodological biases that can distort linguistic interpretations.
%R 10.63317/2vzsgmq46vrk
%U https://aclanthology.org/2026.signlang-1.47/
%U https://doi.org/10.63317/2vzsgmq46vrk
%P 459-467
Markdown (Informal)
[Comparing Computer Vision Instruments for Eye Blink Analysis](https://aclanthology.org/2026.signlang-1.47/) (Susman et al., SignLang 2026)
ACL
- Margaux Susman, Carla Miquel Blasco, and Jan Bulla. 2026. Comparing Computer Vision Instruments for Eye Blink Analysis. In Proceedings of the LREC 2026 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion, pages 459–467, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).