CNN Depression Severity Level Estimation from Upper Body vs. Face-Only Images

Ahmad, Dua’a; Goecke, Roland; Ireland, James

doi:10.1007/978-3-030-68780-9_56

Cited by 2 publications

(1 citation statement)

References 34 publications

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“…In addition, this discipline allows the development of a non-invasive and unobtrusive technology and modality that can support the medical diagnosis while the physician focuses exclusively on the patient. The literature studies based on facial visual information have concentrated mainly on three ideas: extracting features from textures and dynamic textures using handcrafted textural descriptors [7], extracting temporal features from the facial geometry and morphology to analyze facial expressions using Facial Action Coding System (FACS) and Action Units (AUs) [8] [9] or facial and head movement dynamics [10], and using deep learning approaches [11] [12] [13], which represent the state-of-the-art methods nowadays.…”

Section: Introductionmentioning

confidence: 99%

Depression Recognition Using Remote Photoplethysmography From Facial Videos

Casado

Cañellas

López

2023

IEEE Trans. Affective Comput.

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Depression is a mental illness that may be harmful to an individual's health. The detection of mental health disorders in the early stages and a precise diagnosis are critical to avoid social, physiological, or psychological side effects. This work analyzes physiological signals to observe if different depressive states have a noticeable impact on the blood volume pulse (BVP) and the heart rate variability (HRV) response. Although typically, HRV features are calculated from biosignals obtained with contact-based sensors such as wearables, we propose instead a novel scheme that directly extracts them from facial videos, just based on visual information, removing the need for any contact-based device. Our solution is based on a pipeline that is able to extract complete remote photoplethysmography signals (rPPG) in a fully unsupervised manner. We use these rPPG signals to calculate over 60 statistical, geometrical, and physiological features that are further used to train several machine learning regressors to recognize different levels of depression. Experiments on two benchmark datasets indicate that this approach offers comparable results to other audiovisual modalities based on voice or facial expression, potentially complementing them. In addition, the results achieved for the proposed method show promising and solid performance that outperforms hand-engineered methods and is comparable to deep learning-based approaches.

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