Invariant neural-network based face detection with orthogonal Fourier-Mellin moments
Bibliographic record
Abstract
We apply the orthogonal Fourier-Mellin moments (OFMMs) to the specific problem of fully translation-, scale- and in-plane rotation-invariant detection of human faces in 2D static color images, and compare their performance with that of the generalized Hu's moments or non-orthogonal FMMs. The OFMMs have the advantages of non-redundancy of information, robustness with respect to noise and the ability to reconstruct the original object. Color segmentation is first performed in nine different chrominance spaces by use of two human skin chrominance models. For feature extraction in the segmented images, the same number of OFMMs are used (as for the FMMs) as the input vector to a multilayer perceptron neural network to distinguish faces from distracters. It is shown that, at least in the specific problem of face detection from segmented images, for the same set of test images, there is no significant advantage over the FMMs in using the OFMMs, and that in practice both types of moments may be used. Possible explanations for such results are presented.
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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 | 0.000 |
| 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".