@inproceedings{077d46bf2b294f669edef4a0910d2875,
title = "A temporally piece-wise fisher vector approach for depression analysis",
abstract = "Depression and other mood disorders are common, disabling disorders with a profound impact on individuals and families. Inspite of its high prevalence, it is easily missed during the early stages. Automatic depression analysis has become a very active field of research in the affective computing community in the past few years. This paper presents a framework for depression analysis based on unimodal visual cues. Temporally piece-wise Fisher Vectors (FV) are computed on temporal segments. As a low-level feature, block-wise Local Binary Pattern-Three Orthogonal Planes descriptors are computed. Statistical aggregation techniques are analysed and compared for creating a discriminative representative for a video sample. The paper explores the strength of FV in representing temporal segments in a spontaneous clinical data. This creates a meaningful representation of the facial dynamics in a temporal segment. The experiments are conducted on the Audio Video Emotion Challenge (AVEC) 2014 German speaking depression database. The superior results of the proposed framework show the effectiveness of the technique as compared to the current state-of-art.",
keywords = "Depression Analysis, Fisher Vector",
author = "Abhinav Dhall and Roland Goecke",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 2015 International Conference on Affective Computing and Intelligent Interaction, ACII 2015 ; Conference date: 21-09-2015 Through 24-09-2015",
year = "2015",
month = dec,
day = "2",
doi = "10.1109/ACII.2015.7344580",
language = "English",
series = "2015 International Conference on Affective Computing and Intelligent Interaction, ACII 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "255--259",
booktitle = "2015 International Conference on Affective Computing and Intelligent Interaction, ACII 2015",
address = "United States",
}