Toronto NeuroFace Dataset

A New Dataset for Facial Motion Analysis in Individuals with Neurological Disorders

Authors: Bandini, A., Rezaei, S., Guarin, D., Kulkarni, M., Lim, D., Boulos, M., Zinman, L., Yunusova, Y. & Taati, B.

Abstract: In this paper, we present the first public dataset with videos of oro-facial gestures performed by individuals with orofacial impairment due to neurological disorders, such as amyotrophic lateral sclerosis (ALS) and stroke. Perceptual clinical scores from trained clinicians are provided as metadata. Manual annotation of facial landmarks is also provided for a subset of over 3300 frames. Through extensive experiments with multiple facial landmark detection algorithms, including state-of-the-art convolutional neural network (CNN) models, we demonstrated the presence of bias in the landmark localization accuracy of pretrained face alignment approaches in our participant groups. The pre-trained models produced a higher error in the two clinical groups compared to the age-matched healthy control subjects.We also demonstrated that this bias can be reduced by fine-tuning the existing approaches using data from the target population. The release of this dataset aims to propel the development of face alignment algorithms robust to the presence of oro-facial impairment, support the automatic analysis and recognition of oro-facial gestures, enhance the automatic identification of neurological diseases, as well as the estimation of disease severity from videos and images.

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