Multi-scale Analysis of Local Phase and Local Orientation for Dynamic Facial Expression Recognition
Bibliographic record
Abstract
Automated facial expression analysis is an active research area for human-computer interaction as it enables computers to understand and interact with humans in more natural ways.In this work, a novel local descriptor is proposed for facial expression analysis in a video sequence.The proposed descriptor is based on histograms of local phase and local orientation of gradients obtained from a sequence of face images to describe the spatial and temporal information of the face images.The descriptor is able to effectively represent the temporal local information and its spatial locations which are important cues for facial expression recognition.This is further extended to multi-scale to achieve better performance in natural settings where the image resolution varies.The experimental results conducted on the Cohn-Kanade (CK + ) database to detect six basic emotions achieved an accuracy of 94.58%.For the AVEC 2011 video-subchallenge, the detection of four emotion dimensions obtained comparable accuracy with the highest reported average accuracy in the test evaluation.The advantages of our method include local feature extraction incorporating temporal domain, high accuracy and robustness to illumination changes.Thus the proposed descriptor is suited for continuous facial expression analysis in the area of human-computer interaction.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".