Effectiveness of various classification techniques on human face recognition
Bibliographic record
Abstract
In this paper the effectiveness of different classification techniques is evaluated on the performance of face recognition algorithms. Gabor wavelet and its fusion with local binary pattern (LBP) are utilized as feature extractors. Dimensionality reduction approaches, principal component analysis (PCA) and Fisher's linear discriminant (FLD), are employed to reduce the size of feature vector. The performance of nearest neighbor (NN) classifier with various cost functions, sparse classification, multilayer feed-forward neural network (MFNN) and extreme learning machine (ELM) are analysed on three face databases, Extended YaleB, FERET and Multi-PIE, which contain large number of individuals with images under various illumination conditions and different facial expressions. Simulation results show that ELM and MFNN are effective in all conditions. The performance of nearest neighbor and sparse classifier is degraded under severe illumination variation.
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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 | 0.001 | 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 | 0.000 |
| 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".