A Low Error Face Recognition System Based on A New Arrangement of Convolutional Neural Network and Data Augmentation
Why this work is in the frame
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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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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it