Improving Depression estimation from facial videos with face alignment, training optimization and scheduling

Deep learning models have shown promising results in recognizing depressive states using video-based facial expressions. While successful models typically leverage using 3D-CNNs or video distillation techniques, the different use of pretraining, data augmentation, preprocessing, and optimization techniques across experiments makes it difficult to make fair architectural comparisons. We propose instead to enhance two simple models based on ResNet-50 that use only static spatial information by using two specific face alignment methods and improved data augmentation, optimization, and scheduling techniques. Our extensive experiments on benchmark datasets obtain similar results to sophisticated spatio-temporal models for single streams, while the score-level fusion of two different streams outperforms state-of-the-art methods. Our findings suggest that specific modifications in the preprocessing and training process result in noticeable differences in the performance of the models and could hide the actual originally attributed to the use of different neural network architectures.Comment: 5 page

Audio-Based Classification of Respiratory Diseases using Advanced Signal Processing and Machine Learning for Assistive Diagnosis Support

In global healthcare, respiratory diseases are a leading cause of mortality, underscoring the need for rapid and accurate diagnostics. To advance rapid screening techniques via auscultation, our research focuses on employing one of the largest publicly available medical database of respiratory sounds to train multiple machine learning models able to classify different health conditions. Our method combines Empirical Mode Decomposition (EMD) and spectral analysis to extract physiologically relevant biosignals from acoustic data, closely tied to cardiovascular and respiratory patterns, making our approach apart in its departure from conventional audio feature extraction practices. We use Power Spectral Density analysis and filtering techniques to select Intrinsic Mode Functions (IMFs) strongly correlated with underlying physiological phenomena. These biosignals undergo a comprehensive feature extraction process for predictive modeling. Initially, we deploy a binary classification model that demonstrates a balanced accuracy of 87% in distinguishing between healthy and diseased individuals. Subsequently, we employ a six-class classification model that achieves a balanced accuracy of 72% in diagnosing specific respiratory conditions like pneumonia and chronic obstructive pulmonary disease (COPD). For the first time, we also introduce regression models that estimate age and body mass index (BMI) based solely on acoustic data, as well as a model for gender classification. Our findings underscore the potential of this approach to significantly enhance assistive and remote diagnostic capabilities.Comment: 5 pages, 2 figures, 3 tables, Conference pape

Depression recognition from facial videos: Preprocessing and scheduling choices hide the architectural contributions

Abstract Deep learning models have been widely applied in video‐based depression detection. It is observed that the diversity of preprocessing, data augmentation, and optimization techniques makes it difficult to fairly compare model architectures. In this study, the typical ResNet‐50 model is enhanced by using specific face alignment methods, improved data augmentation, optimization, and scheduling techniques. The extensive experiments on two popular benchmark datasets (AVEC2013 and AVEC2014) obtained competitive results, compared to sophisticated spatio‐temporal models for single streams. Moreover, the score‐level fusion approach based on two texture streams outperformed the state‐of‐the‐art methods. It achieved mean square errors of 5.82 and 5.50 on AVEC2013 and AVEC2014, respectively. These findings suggest that the preprocessing and training configurations result in noticeable improvements, which have been originally attributed to the network architectures

Depression recognition from facial videos:Preprocessing and scheduling choices hide the architectural contributions

Deep learning models have been widely applied in video-based depression detection. It is observed that the diversity of preprocessing, data augmentation, and optimization techniques makes it difficult to fairly compare model architectures. In this study, the typical ResNet-50 model is enhanced by using specific face alignment methods, improved data augmentation, optimization, and scheduling techniques. The extensive experiments on two popular benchmark datasets (AVEC2013 and AVEC2014) obtained competitive results, compared to sophisticated spatio-temporal models for single streams. Moreover, the score-level fusion approach based on two texture streams outperformed the state-of-the-art methods. It achieved mean square errors of 5.82 and 5.50 on AVEC2013 and AVEC2014, respectively. These findings suggest that the preprocessing and training configurations result in noticeable improvements, which have been originally attributed to the network architectures.</p

Depression recognition from facial videos:Preprocessing and scheduling choices hide the architectural contributions

Deep learning models have been widely applied in video-based depression detection. It is observed that the diversity of preprocessing, data augmentation, and optimization techniques makes it difficult to fairly compare model architectures. In this study, the typical ResNet-50 model is enhanced by using specific face alignment methods, improved data augmentation, optimization, and scheduling techniques. The extensive experiments on two popular benchmark datasets (AVEC2013 and AVEC2014) obtained competitive results, compared to sophisticated spatio-temporal models for single streams. Moreover, the score-level fusion approach based on two texture streams outperformed the state-of-the-art methods. It achieved mean square errors of 5.82 and 5.50 on AVEC2013 and AVEC2014, respectively. These findings suggest that the preprocessing and training configurations result in noticeable improvements, which have been originally attributed to the network architectures.</p

Depression recognition using remote photoplethysmography from facial videos

Abstract 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