Comparison of Feature Extraction with PCA and LTP Methods and Investigating the Effect of Dimensionality Reduction in the Bat Algorithm for Face Recognition
Bibliographic record
Abstract
Face recognition is one of the challenging subjects of image processing. Facial recognition is often a biometric method that basically uses faces to recognize people. The face recognition system consists of three main steps: finding the face in the image, feature extraction and classification. The face recognition system faces challenges such as changes in lighting, changes in age, changes in facial expressions, etc. One of the important issues in this system is the algorithm execution speed. For this purpose, the dimensions of the feature vectors should be small enough, especially when the database is large. Since the face recognition system must be performed on a wide range of databases, dimensionality reduction techniques are required to reduce time and increase accuracy. Dimension reduction methods are used for this purpose. Two methods of dimensionality reduction, including LTP and PCA, are given in this research. In this research, first, the LTP feature vectors are extracted from the face image, and then the effective features are selected using the Bat algorithm. Therefore, this algorithm has three main phases of feature extraction, feature selection and classification. This algorithm is implemented on the ORL database, which contains 400 images of 40 different people with a size of 112×92 pixels. In addition to reducing the time required for testing, the proposed method has provided a very good accuracy of 99%.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".