Automatic Depression Analysis Using Dynamic Facial Appearance Descriptor and Dirichlet Process Fisher Encoding

• Published in: IEEE transactions on multimedia Vol. 21; no. 6; pp. 1476 - 1486
• Main Authors: He, Lang, Jiang, Dongmei, Sahli, Hichem
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.06.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Automation; Coding; Depression; Dirichlet problem; Dirichlet process Fisher vector (DPFV); dynamic feature descriptor; Encoding; Face; Face recognition; Feature extraction; Gaussian process; Histograms; Macrostructure; median robust local binary patterns from three orthogonal planes (MRLBP-TOP); Mental depression; nonverbal behaviors; Orthogonality; Planes; Probabilistic models; System effectiveness; Visualization
• ISSN: 1520-9210; 1941-0077
• DOI: 10.1109/TMM.2018.2877129

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.