Face Recognition by Using 2D Orthogonal Subspace Projections
Bibliographic record
Abstract
In this paper, the implementations and comparison of some classifiers along with 2D subspace projection approaches have been carried out for the face recognition problem. For this purpose, the well-known classifiers such as K-Nearest Neighbor (K-NN), Common Matrix Approach (CMA), Support Vector Machine (SVM) and Convolutional Neural Network (CNN) are conducted on low dimensional face representations that are determined from 2DPCA-, 2DSVD- and 2DFDA approaches. CMA, which is a 2D version of the Common Vector Approach (CVA), finds a common matrix for each face class. From the experimental results, we have observed that the SVM presents a dominant performance in general. When overall results of all datasets are considered, CMA is slightly superior to others in case of 2DPCA- and 2DSVD-based features matrices of the AR dataset. On the other side, CNN is better than other classifiers when it comes to develop a face recognition system based on original face samples and 2DPCA-based feature matrices of the Yale dataset. The experimental results indicate that use of these feature matrices with CMA, SVM, and CNN in classification problems is more advantageous than the use of original pixel matrices in the sense of both processing time and memory requirement.
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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) | 0.001 | 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".