Bibliographic record
Abstract
In a face–to–face talk, language is supported by nonverbal communication, which plays a central role in human social behavior by adding cues to the meaning of speech, providing feedback, and managing synchronization. Information about the emotional state of a person is usually carried out by facial attributes. In fact, 55% of a message is communicated by facial expressions whereas only 7% is due to linguistic language and 38% to paralanguage. However, there are currently no established instruments to measure such behavior. The computer vision community is therefore interested in the development of automated techniques for prototypic facial expression analysis, for human computer interaction applications, meeting video analysis, security and clinical applications. For gathering observable cues, we try to design, in this research, a framework that can build a relatively comprehensive source of visual information, which will be able to distinguish the facial deformations, thus allowing to point out the presence or absence of a particular facial action. A detailed review of identified techniques led us to explore two different approaches. The first approach involves appearance modeling, in which we use the gradient orientations to generate a dense representation of facial attributes. Besides the facial representation problem, the main difficulty of a system, which is intended to be general, is the implementation of a generic model independent of individual identity, face geometry and size. We therefore introduce a concept of prototypic referential mapping through a SIFT–flow registration that demonstrates, in this thesis, its superiority to the conventional eyes–based alignment. In a second approach, we use a geometric model through which the facial primitives are represented by Gabor filtering. Motivated by the fact that facial expressions are not only ambiguous and inconsistent across human but also dependent on the behavioral context; in this approach, we present a personalized facial expression recognition system whose overall performance is directly related to the localization performance of a set of facial fiducial points. These points are tracked through a sequence of video frames by a modification of a fast Gabor phase–based disparity estimation technique. In this thesis, we revisit the confidence measure, and introduce an iterative conditional procedure for displacement estimation that improve the robustness of the original methods. Keywords: Computer vision, image processing, facial expression recognition, emotion analysis, face analysis, feature representation, Gabor filtering, registration, tracking.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".