A Noncontact Emotion Recognition Method Based on Complexion and Heart Rate
Bibliographic record
Abstract
This article proposes a noncontact emotion recognition method based on complexion and heart rate (HR). Unlike most existing noncontact emotion recognition methods, our method uses complexion and heart rate as contactless emotion recognition indicators, effectively avoiding the possibility of disguised facial expressions or acoustic features in noncontact methods. Different from most existing contact emotion recognition methods, our method only employs an ordinary device, Kinect, to achieve the complexion and heart rate, which can effectively avoid subjects’ psychological oppression when using wearable devices. Convolutional neural network (CNN) and bidirectional long short-term memory-conditional random fields (Bi-LSTM-CRF) are developed to extract the features of complexion and heart rate for contactless emotion recognition, respectively. We experimentally compared our methods with the existing five emotion recognition methods in practical application. The results show that our methods can outperform the existing five methods and obtain an emotion recognition accuracy higher than 80% in detection of anger, depression, doubt, and indignation. This indicates that the fusion of complexion and heart rate can effectively improve the emotional recognition accuracy. Note that our method requires neither expensive equipment nor conscious emotional acquisition by the participant, thereby promoting the development of emotion recognition in various fields such as the interrogation process and psychological counseling.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".