TY - GEN
T1 - Heart Rate Estimation From Facial Videos for Depression Analysis
AU - Mustafa, Aamir
AU - Bhatia, Shalini
AU - Hayat, Munawar
AU - Goecke, Roland
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Automated facial video analysis is useful in numerous health care applications. For example, spatio-temporal analysis of such videos has been previously done for assisting clinicians in the diagnosis of depression. Physiological measures, such as an individual's heart rate, provide very important cues to understand a person's mental health. Unobtrusively estimated heart rate has not been previously used to analyse individuals' mental health. In this paper, we automatically estimate heart rate activity from facial videos. We then study the association of the estimated heart rate activity with the person's mental health, as diagnosed by clinicians. Specifically, from the heart rate activity in response to watching different movies, we classify individuals as either depressed or healthy. The efficacy of the proposed scheme is demonstrated by experimental evaluations on a clinically validated dataset. Our results suggest unobtrusively estimated heart rate to be very effective for depression analysis.
AB - Automated facial video analysis is useful in numerous health care applications. For example, spatio-temporal analysis of such videos has been previously done for assisting clinicians in the diagnosis of depression. Physiological measures, such as an individual's heart rate, provide very important cues to understand a person's mental health. Unobtrusively estimated heart rate has not been previously used to analyse individuals' mental health. In this paper, we automatically estimate heart rate activity from facial videos. We then study the association of the estimated heart rate activity with the person's mental health, as diagnosed by clinicians. Specifically, from the heart rate activity in response to watching different movies, we classify individuals as either depressed or healthy. The efficacy of the proposed scheme is demonstrated by experimental evaluations on a clinically validated dataset. Our results suggest unobtrusively estimated heart rate to be very effective for depression analysis.
UR - http://www.scopus.com/inward/record.url?scp=85047497801&partnerID=8YFLogxK
U2 - 10.1109/ACII.2017.8273645
DO - 10.1109/ACII.2017.8273645
M3 - Conference contribution
SN - 9781538605646
T3 - 2017 7th International Conference on Affective Computing and Intelligent Interaction, ACII 2017
SP - 498
EP - 503
BT - 2017 7th International Conference on Affective Computing and Intelligent Interaction, ACII 2017
PB - IEEE, Institute of Electrical and Electronics Engineers
CY - San Antonio, TX, USA
ER -
