Journal
IEEE TRANSACTIONS ON MULTIMEDIA
Volume 21, Issue 6, Pages 1476-1486Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMM.2018.2877129
Keywords
Depression; nonverbal behaviors; dynamic feature descriptor; median robust local binary patterns from three orthogonal planes (MRLBP-TOP); Dirichlet process Fisher vector (DPFV)
Categories
Funding
- Shaanxi Provincial Key RD Program [2017KW-ZD-14]
- National Natural Science Foundation of China [61273265]
- China Scholarship Council [201606290171]
- VUB Interdisciplinary Research Program through the EMO-App project
Ask authors/readers for more resources
Depression causes mood disorders with noticeable problems in day-to-day activities. Current methods of assessing depression depend almost entirely on clinical interviews or questionnaires. They lack systematic and efficient ways of incorporating behavioral observations that are strong indicators of a psychological disorder. To help clinicians effectively and efficiently diagnose depression severity, automated systems, using objective and quantifiable data for depression assessment, are being developed. This paper presents a framework toward estimating a clinical depression-specific score, namely the Beck Depression Inventory-II (BDI-II) score, based on the analysis of facial expressions features. To extract facial dynamic features, we propose a novel dynamic feature descriptor denoted as median robust local binary patterns from three orthogonal planes (MRLBP-TOP), which can capture both the microstructure and macrostructure of facial appearance and dynamics. To aggregate the MRLBP-TOP over an image sequence, we propose a variant to the Fisher vector (FV) encoding scheme, denoted as the Dirichlet process FV (DPFV). DPFV adopts Dirichlet process Gaussian mixture models (DPGMM) to automatically learn the number of GMM mixtures and model parameters. Experimental results on the AVEC2013 and AVEC2014 depression databases have demonstrated the effectiveness of the proposed method.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available