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Section: New Results

Characterizing the State of Apathy with Facial Expression and Motion Analysis

Participants : S L Happy, Antitza Dantcheva, Abhijit Das, François Brémond, Radia Zeghari [Cobtek] , Philippe Robert [Cobtek] .

Reduced emotional response, lack of motivation, and limited social interaction comprise the major symptoms of apathy. Current methods for apathy diagnosis require the patient's presence in a clinic, and time consuming clinical interviews and questionnaires involving medical personnel, which are costly and logistically inconvenient for patients and clinical staff, hindering among other large scale diagnostics. In this work we introduced a novel machine learning framework to classify apathetic and non-apathetic patients based on analysis of facial dynamics, entailing both emotion and facial movement. Our approach catered to the challenging setting of current apathy assessment interviews, which include short video clips with wide face pose variations, very low-intensity expressions, and insignificant inter-class variations. We tested our algorithm on a dataset consisting of 90 video sequences acquired from 45 subjects and obtained an accuracy of $84\%$ in apathy classification. Based on extensive experiments, we showed that the fusion of emotion and facial local motion produced the best feature set for apathy classification. In addition, we trained regression models to predict the clinical scores related to the mental state examination (MMSE) and the neuropsychiatric apathy inventory (NPI) using the motion and emotion features. Our results suggested that the performance can be further improved by appending the predicted clinical scores to the video-based feature representation. This work has been presented at the IEEE International Conference on Automatic Face and Gesture Recognition (FG 2019) [25].

Figure 12. Overall framework for apathy detection from facial videos.