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Exploring Facial Expressions and Affective Domains for Parkinson Detection
The principal contributions of this work are: (1) a novel framework to exploit deep face architectures to model hypomimia in PD patients; (2) we experimentally compare PD detection based on single images vs. image sequences while the patients are evoked various face expressions; (3) we explore different domain adaptation techniques to exploit existing models initially trained either for Face Recognition or to detect FAUs for the automatic discrimination between PD patients and healthy subjects; and (4) a new approach to use triplet-loss learning to improve hypomimia modeling and PD detection.
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