Analysis of facial expressions in parkinson's disease through video-based automatic methods

• Published in: Journal of neuroscience methods Vol. 281; pp. 7 - 20
• Main Authors: Bandini, Andrea, Orlandi, Silvia, Escalante, Hugo Jair, Giovannelli, Fabio, Cincotta, Massimo, Reyes-Garcia, Carlos A., Vanni, Paola, Zaccara, Gaetano, Manfredi, Claudia
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.04.2017
• Subjects: Aged; Aged, 80 and over; Automatic facial expression recognition; Contactless; Emotions; Facial Expression; Facial mimicry; Female; Humans; Hypomimia; Image Interpretation, Computer-Assisted - methods; Imitative Behavior; Male; Middle Aged; Parkinson Disease - classification; Parkinson Disease - diagnosis; Parkinson Disease - physiopathology; Parkinson's disease; Support Vector Machine; Video Recording - methods; Video-based; Parkinson's disease; Automatic facial expression recognition; Hypomimia; Contactless; Facial mimicry; Video-based
• ISSN: 0165-0270; 1872-678X
• DOI: 10.1016/j.jneumeth.2017.02.006

•Objectify facial hypomimia in Parkinson’s disease through video analysis.•Basic facial expressions of Patients and healthy subjects were investigated.•The distance from a neutral baseline was computed to quantify expression changes.•Parkinsonian patients reported on average lower amounts of facial mimicry.•We demonstrated that contactless methods are suitable for objectifying hypomimia. The automatic analysis of facial expressions is an evolving field that finds several clinical applications. One of these applications is the study of facial bradykinesia in Parkinson’s disease (PD), which is a major motor sign of this neurodegenerative illness. Facial bradykinesia consists in the reduction/loss of facial movements and emotional facial expressions called hypomimia. In this work we propose an automatic method for studying facial expressions in PD patients relying on video-based 17 Parkinsonian patients and 17 healthy control subjects were asked to show basic facial expressions, upon request of the clinician and after the imitation of a visual cue on a screen. Through an existing face tracker, the Euclidean distance of the facial model from a neutral baseline was computed in order to quantify the changes in facial expressivity during the tasks. Moreover, an automatic facial expressions recognition algorithm was trained in order to study how PD expressions differed from the standard expressions. Results show that control subjects reported on average higher distances than PD patients along the tasks. This confirms that control subjects show larger movements during both posed and imitated facial expressions. Moreover, our results demonstrate that anger and disgust are the two most impaired expressions in PD patients. Contactless video-based systems can be important techniques for analyzing facial expressions also in rehabilitation, in particular speech therapy, where patients could get a definite advantage from a real-time feedback about the proper facial expressions/movements to perform.