Two-dimensional face recognition algorithms in the frequency domain
Bibliographic record
Abstract
Principal component analysis (PCA), well-known for its compaction capability and robustness against noise, is a widely used technique for face recognition. However, it has major drawbacks: (i) losing image details, (ii) having a large time complexity and (iii) suffering from adverse effect of intra-class pose variations. To overcome the last drawback in PCA, Fourier magnitude (FM-PCA) has been proposed in which Fourier magnitudes have been used for feature extraction. Furthermore, to address the other two drawbacks, two-dimensional PCA (2DPCA) algorithms have been proposed. In this paper, to overcome the problems (i) and (ii) in FM-PCA and the problem (iii) in 2DPCA algorithms, Fourier magnitude 2DPCA algorithms which incorporate the advantages of FM-PCA and 2DPCA algorithms are developed. Extensive simulations on the ORL database confirm the effectiveness of the proposed algorithms in providing higher accuracy over PCA, FM-PCA and 2DPCA algorithms with a much smaller complexity compared to that of FM-PCA.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".