@inproceedings{susman:26023:sign-lang:lrec,
  author    = {Susman, Margaux and Miquel Blasco, Carla and Bulla, Jan},
  title     = {Comparing Computer Vision Instruments for Eye Blink Analysis},
  pages     = {459--467},
  editor    = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Mesch, Johanna and Schulder, Marc},
  booktitle = {Proceedings of the {LREC2026} 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion},
  maintitle = {15th International Conference on Language Resources and Evaluation ({LREC} 2026)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Palma, Mallorca, Spain},
  day       = {16},
  month     = may,
  year      = {2026},
  isbn      = {978-2-493814-82-1},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/26023.html},
  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.}
}