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Fusing active orientation models and mid-term audio features for automatic depression estimation

Authors:

Christos Smailis,

Nikolaos Sarafianos,

Theodoros Giannakopoulos,

Stavros PerantonisAuthors Info & Claims

PETRA '16: Proceedings of the 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments

Article No.: 39, Pages 1 - 4

https://doi.org/10.1145/2910674.2935856

Published: 29 June 2016 Publication History

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Abstract

In this paper, we predict a human's depression level in the BDI-II scale, using facial and voice features. Active orientation models (AOM) and several voice features were extracted from the video and audio modalities. Long-term and mid-term features were computed and a fusion is performed in the feature space. Videos from the Depression Recognition Sub-Challenge of the 2014 Audio-Visual Emotion Challenge and Workshop (AVEC 2014) were used and support vector regression models were trained to predict the depression level. We demonstrated that the fusion of AOMs with audio features leads to better performance compared to individual modalities. The obtained regression results indicate the robustness of the proposed technique, under different settings, as well as an RMSE improvement compared to the AVEC 2014 video baseline.

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Cited By

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Pampouchidou ASimos PMarias KMeriaudeau FYang FPediaditis MTsiknakis M(2019)Automatic Assessment of Depression Based on Visual Cues: A Systematic ReviewIEEE Transactions on Affective Computing10.1109/TAFFC.2017.272403510:4(445-470)Online publication date: 1-Oct-2019
https://doi.org/10.1109/TAFFC.2017.2724035
Bernin AMüller LGhose Svon Luck KGrecos CWang QVogt F(2017)Towards More Robust Automatic Facial Expression Recognition in Smart EnvironmentsProceedings of the 10th International Conference on PErvasive Technologies Related to Assistive Environments10.1145/3056540.3056546(37-44)Online publication date: 21-Jun-2017
https://dl.acm.org/doi/10.1145/3056540.3056546

Fusing active orientation models and mid-term audio features for automatic depression estimation
1. Computing methodologies
  1. Artificial intelligence

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PETRA '16: Proceedings of the 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments

June 2016

455 pages

ISBN:9781450343374

DOI:10.1145/2910674

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 June 2016

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PETRA '16

PETRA '16: 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments

June 29 - July 1, 2016

Island, Corfu, Greece

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Cited By

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Pampouchidou ASimos PMarias KMeriaudeau FYang FPediaditis MTsiknakis M(2019)Automatic Assessment of Depression Based on Visual Cues: A Systematic ReviewIEEE Transactions on Affective Computing10.1109/TAFFC.2017.272403510:4(445-470)Online publication date: 1-Oct-2019
https://doi.org/10.1109/TAFFC.2017.2724035
Bernin AMüller LGhose Svon Luck KGrecos CWang QVogt F(2017)Towards More Robust Automatic Facial Expression Recognition in Smart EnvironmentsProceedings of the 10th International Conference on PErvasive Technologies Related to Assistive Environments10.1145/3056540.3056546(37-44)Online publication date: 21-Jun-2017
https://dl.acm.org/doi/10.1145/3056540.3056546

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Fusing short term and long term features for improved speaker diarization