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Person Identification Based on Sign Language Motion: Insights from Human Perception and Computational Modeling

Abstract : Previous research has shown that human perceivers can identify individuals from biological movements, such as walking or dancing. It remains to be investigated whether sign language motion, which obeys to other constraints than pure biomechanical ones, also allows for person identification. The present study is the first to investigate whether deaf perceivers recognize signers based on motion capture (mocap) data only. Point-light displays of 4 signers producing French Sign Language utterances were presented to a group of deaf participants. Results revealed that participants managed to identify familiar signers above chance level. Computational analysis of the mocap data provided further evidence that morphological cues were unlikely to be sufficient for signer identification. A machine learning approach aiming to evaluate the motion features that can account for human performance is currently being developed. First results of the model reveal high accuracy for signer identification based on the same stimulus material, even after having normalized for size and shape. The present behavioral and computational findings suggest that mocap data contain sufficient information to identify signers, and this beyond simple cues related to morphology.
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Submitted on : Wednesday, December 16, 2020 - 7:10:03 PM
Last modification on : Tuesday, January 4, 2022 - 6:44:14 AM
Long-term archiving on: : Wednesday, March 17, 2021 - 8:08:18 PM


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Félix Bigand, E. Prigent, Annelies Braffort. Person Identification Based on Sign Language Motion: Insights from Human Perception and Computational Modeling. International Conference on Movement and Computing, ACM, Jul 2020, Jersey City / Virtual, United States. ⟨10.1145/3401956.3404187⟩. ⟨hal-03078733⟩



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