A review on depression detection and diagnoses based on visual facial cues

Nasser, Sana A.; Hashim, Ivan A.; Ali, Wissam H.

doi:10.1109/iiceta50496.2020.9318860

Cited by 8 publications

(4 citation statements)

References 39 publications

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“…We also quite differently propose the feature fusion method for future work. Nasser et al ( 2020 ) conducted a review study on depression detection based on traditional machine learning methods using visual facial cues. The authors concluded that the Support Vector Machine technique is recommended for visual feature extraction methods for depression detection, due to the high accuracy obtained with a large number of subjects and with the usage of action units of full face.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: a review

et al. 2022

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Epidemiological studies report high levels of anxiety and depression amongst adolescents. These psychiatric conditions and complex interplays of biological, social and environmental factors are important risk factors for suicidal behaviours and suicide, which show a peak in late adolescence and early adulthood. Although deaths by suicide have fallen globally in recent years, suicide deaths are increasing in some countries, such as the US. Suicide prevention is a challenging global public health problem. Currently, there aren’t any validated clinical biomarkers for suicidal diagnosis, and traditional methods exhibit limitations. Artificial intelligence (AI) is budding in many fields, including in the diagnosis of medical conditions. This review paper summarizes recent studies (past 8 years) that employed AI tools for the automated detection of depression and/or anxiety disorder and discusses the limitations and effects of some modalities. The studies assert that AI tools produce promising results and could overcome the limitations of traditional diagnostic methods. Although using AI tools for suicidal ideation exhibits limitations, these are outweighed by the advantages. Thus, this review article also proposes extracting a fusion of features such as facial images, speech signals, and visual and clinical history features from deep models for the automated detection of depression and/or anxiety disorder in individuals, for future work. This may pave the way for the identification of individuals with suicidal thoughts.

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Section: Discussionmentioning

confidence: 99%

Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: a review

et al. 2022

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“…Generally, researchers believe that the manifest characteristics of depression can be identified by a large number of visual signals [19], such as involuntary changes in facial action units (AU) [20][21][22], eye gaze direction [23][24][25], pupil dilation response [9,26,27], facial expressions [28,29] and head movement posture [30]. These biomarkers effectively capture mental disorders caused by depression and represent critical signals for the automatic detection of depression.…”

Section: Related Workmentioning

confidence: 99%

Automatic diagnosis of depression based on attention mechanism and feature pyramid model

Xu,

Huo,

et al. 2024

PLoS ONE

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Currently, most diagnoses of depression are evaluated by medical professionals, with the results of these evaluations influenced by the subjective judgment of physicians. Physiological studies have shown that depressed patients display facial movements, head posture, and gaze direction disorders. To accurately diagnose the degree of depression of patients, this paper proposes a comprehensive framework, Cross-Channel Attentional Depression Detection Network, which can automatically diagnose the degree of depression of patients by inputting information from the facial images of depressed patients. Specifically, the comprehensive framework is composed of three main modules: (1) Face key point detection and cropping for video images based on Multi-Task Convolutional Neural Network. (2) The improved Feature Pyramid Networks model can fuse shallow features and deep features in video images and reduce the loss of miniscule features. (3) A proposed Cross-Channel Attention Convolutional Neural Network can enhance the interaction between tensor channel layers. Compared to other methods for automatic depression identification, a superior method was obtained by conducting extensive experiments on the depression dataset AVEC 2014, where the Root Mean Square Error and the Mean Absolute Error were 8.65 and 6.66, respectively.

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“…More negative valence in facial emotion expressions is a characteristic of depressed individuals (for a review, see Nasser et al, 2020). Although several studies (e.g., Gavrilescu & Vizireanu, 2019; Wang et al, 2018) have differentiated between depressed clients and healthy controls using facial expression valence (FEV), they have all relied on one-time, cross-sectional data.…”

Section: From Facial Expressions and Vocal Analysis To Multimodal Aff...mentioning

confidence: 99%

Multimodal analysis of temporal affective variability within treatment for depression.

Paz,

Rafaeli,

Bar-Kalifa

et al. 2024

Journal of Consulting and Clinical Psychology

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A review on depression detection and diagnoses based on visual facial cues

Cited by 8 publications

References 39 publications

Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: a review

Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: a review

Automatic diagnosis of depression based on attention mechanism and feature pyramid model

Multimodal analysis of temporal affective variability within treatment for depression.

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