A Low Error Face Recognition System Based on A New Arrangement of Convolutional Neural Network and Data Augmentation
Bibliographic record
Abstract
This paper represents a low error face recognition system constructed on convolutional neural network structure. The proposed method introduces a new layer arrangement for the CNN with added normalization layers. In addition, a data augmentation step consisting of vertical flip, scaling, rotation, and shift is implemented into the framework of our system to improve the accuracy. This augmentation helps the system to overcome the issue of having low number of samples per individuals in our dataset. The support vector machine (SVM) and Softmax are considered as classifiers of the proposed system, and results are evaluated for both classification methods. The system is tested on the ORL face image dataset. In our experiment SVM showed a higher accuracy compared to Softmax. The results compared to other existing face recognition methods show better performance and higher accuracy of the proposed system. The proposed method is evaluated with %50 of the dataset as training samples and the rest as test samples by random. Our technique achieves %98.64 with Softmax and %99.7 with SVM recognition rate.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".