Combining Global and Local Convolutional 3D Networks for Detecting Depression from Facial Expressions
Bibliographic record
Abstract
Deep learning architectures have been successfully applied in video-based health monitoring, to recognize distinctive variations in the facial appearance of subjects. To detect patterns of variation linked to depressive behavior, deep neural networks (NNs) typically exploit spatial and temporal information separately by, e.g., cascading a 2D convolutional NN (CNN) with a recurrent NN (RNN), although the intrinsic spatio-temporal relationships can deteriorate. With the recent advent of 3D CNNs like the convolutional 3D (C3D) network, these spatio-temporal relationships can be modeled to improve performance. However, the accuracy of C3D networks remain an issue when applied to depression detection. In this paper, the fusion of diverse C3D predictions are proposed to improve accuracy, where spatio-temporal features are extracted from global (full-face) and local (eyes) regions of subject. This allows to increasingly focus on a local facial region that is highly relevant for analyzing depression. Additionally, the proposed network integrates 3D Global Average Pooling in order to efficiently summarize spatio-temporal features without using fully-connected layers, and thereby reduce the number of model parameters and potential over-fitting. Experimental results on the Audio Visual Emotion Challenge (AVEC 2013 and AVEC 2014) depression datasets indicates that combining the responses of global and local C3D networks achieves a higher level of accuracy than state-of-the-art systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".