Human Face Recognition Using Different Moment Invariants: A Comparative Study
Bibliographic record
Abstract
Human face recognition has recently become one of the hottest topics in the area of pattern recognition due to its applications in identity validation and recognition. Moment Invariants are pattern sensitive features and are used in pattern recognition applications. In this paper different moment invariants have been used to extract features from human face images for recognition application. Moment invariants of Hu (HMI), Bamieh (BMI), Zernike (ZMI), Pseudo Zernike (PZMI), Teague-Zernike (TZMI), Normalized Zernike (NZMI) ,Normalized Pseudo Zernike (NPZMI) and also regular Moment Invariant (RMI) have been applied to the AT&T face database and the results have been compared. Our results show that pseudo Zernike moments yields the best recognition accuracy of 95%.
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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".