Weighted Fusion of Bit Plane-Specific Local Image Descriptors for Facial Expression Recognition
Bibliographic record
Abstract
Automated recognition of facial expression has attracted significant attention in recent years due to its potential applicability in security and surveillance, human computer interaction, social robotics, and animation. This paper presents a new facial expression recognition method that utilizes bit plane specific local image description in a weighted score level fusion. The motivation is to utilize bit plane slicing to highlight the contribution of a particular bit plane made to the holistic facial appearance, which is then used in a weighted score level fusion in order to boost the recognition performance. A new local image descriptor is proposed specifically to extract local features from bit plane representations that utilizes Fisher linear discriminant to maximize the inter-class distance, while minimizing the intra-class variance. Two well-known facial expression databases, namely the Cohn-Kanade (CK) and the Japanese female facial expression (JAFFE) database have been used to evaluate the performance of the proposed method against existing facial appearance descriptors, such as local binary pattern (LBP), local ternary pattern (LTP), local directional pattern (LDP), and linear discriminant analysis (LDA). Experiments with a total of seven prototypic facial expressions show promising results for the proposed method, as compared with the other existing methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 0.000 |
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 teacher head, 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".