Part-based PCA for facial feature extraction and classification
Bibliographic record
Abstract
With the latest advances in the fields of computer vision, image processing and pattern recognition, facial expression recognition is becoming more and more feasible for human computer interaction in Virtual Environments (VEs). In order to achieve subject-independent facial feature extraction and classification, we present part-based PCA (Principal Component Analysis) for facial feature extraction and apply a modified PCA reconstruction method for expression classification. Part-based PCA is employed to minimize the influence of individual differences which hinder facial expression recognition. For the purpose of obtaining part-based PCA, a novel feature detection and extraction approach based on multi-step integral projection is proposed. The features can be automatically detected and located by multi-step integral projection curves without being manually picked and PCA is applied in the detected area instead of the whole face. To solve the problem that the features extracted from PCA are not the best features suitable for classification, we propose a modified PCA reconstruction method. We divide the training set into 7 classes and carry out PCA reconstruction on each class independently. We can identify the expression class by measuring the similarity between the input image and the reconstructed image. Experiments demonstrate that when tested on the JAFFE database, the part-based PCA outperforms traditional PCA of higher recognition rate.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".