Comparative Analysis of Euclidean, Manhattan, Canberra, and Squared Chord Methods in Face Recognition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Face recognition is currently widely used as a security component.In facial recognition, the image used will be converted into a grayish image and subsequently converted into a binary image.The binary image obtained in the next process will be analyzed.The analysis was carried out by calculating the similarity distance between the training data and the test data.In the process of measuring the distance of similarity between data sets, there are often obstacles to the implementation of complex algorithm formulas.This study solves this problem by analyzing the distance functions of Euclidean, Manhattan, Canberra, and the Squared Chord to perform facial recognition.Based on the research that has been carried out, the Euclidean distance function gets an accuracy of 58%, the Manhattan distance function gets an accuracy of 70%, the Canberra distance function gets an accuracy of 92%, and the Squared Chord distance function gets an accuracy of 66%.Based on these results, it can be concluded that Canberra's distance function with a highest accuracy result compared to the other three distance functions is better and more suitable for facial recognition.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| 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 it