Machine Learning of Motion Statistics Reveals the Kinematic Signature of the Identity of a Person in Sign Language.

ABSTRACT: Sign language (SL) motion contains information about the identity of a signer, as does voice for a speaker or gait for a walker. However, how such information is encoded in the movements of a person remains unclear. In the present study, a machine learning model was trained to extract the motion features allowing for the automatic identification of signers. A motion capture (mocap) system recorded six signers during the spontaneous production of French Sign Language (LSF) discourses. A principal component analysis (PCA) was applied to time-averaged statistics of the mocap data. A linear classifier then managed to identify the signers from a reduced set of principal components (PCs). The performance of the model was not affected when information about the size and shape of the signers were

SUBMITTER: Bigand F 

PROVIDER: S-EPMC8342317 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature