Article2015

Automated Depression Diagnosis Based on Facial Dynamic Analysis and Sparse Coding

Lingyun Wen · Xin Li · Guodong Guo · Yu Michael Zhu

141 citations0/0 files storedIEEE Transactions on Information Forensics and Security

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Emotion and Mood Recognition Face recognition and analysis Face and Expression Recognition

Abstract

Depression is a severe psychiatric disorder preventing a person from functioning normally in both work and daily lives. Currently, diagnosis of depression requires extensive participation from clinical experts. It has drawn much attention to develop an automatic system for efficient and reliable diagnosis of depression. Under the influence of depression, visual-based behavior disorder is readily observable. This paper presents a novel method of exploring facial region visual-based nonverbal behavior analysis for automatic depression diagnosis. Dynamic feature descriptors are extracted from facial region subvolumes, and sparse coding is employed to implicitly organize the extracted feature descriptors for depression diagnosis. Discriminative mapping and decision fusion are applied to further improve the accuracy of visual-based diagnosis. The integrated approach has been tested on the AVEC2013 depression database and the best visual-based mean absolute error/root mean square error results have been achieved.

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References

2003The Epidemiology of Major Depressive Disorder 1996Comparison of Beck Depression Inventories-IA and-II in Psychiatric Outpatients 2013AVEC 2013 2009Detecting depression from facial actions and vocal prosody 2013Detecting Depression Severity from Vocal Prosody 2013Nonverbal social withdrawal in depression: Evidence from manual and automatic analyses 2013Depression recognition based on dynamic facial and vocal expression features using partial least square regression 2011Emotion recognition using PHOG and LPQ features

Cited by

2023Depression Detection From Social Networks Data Based on Machine Learning and Deep Learning Techniques: An Interrogative Survey 2021MDN: A Deep Maximization-Differentiation Network for Spatio-Temporal Depression Detection 2023Transformer-based multimodal feature enhancement networks for multimodal depression detection integrating video, audio and remote photoplethysmograph signals 2018Human Behaviour-Based Automatic Depression Analysis Using Hand-Crafted Statistics and Deep Learned Spectral Features 2020Automatic Detection of Depression in Speech Using Ensemble Convolutional Neural Networks

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