A Hybrid 1D CNN-LSTM Model for Face Recognition Using PCA Features
Bibliographic record
Abstract
Recognizing faces is an extremely difficult problem because there are differences in lighting, pose and expression.In this paper we introduce a new One-Dimensional Hybrid Deep Learning (1D-HD) model to face recognition based on pixel-based features extracted through Principal Component Analysis (PCA).The stated pipeline would start by preprocessing the data with Viola-Jones face detection and then convert the data to grayscale, equalize the histogram, downscale, and reduce the dimensionality with the help of PCA.Such low dimensional characteristics are then used to feed a hybrid deep learning network that consists of 1D Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) layers to robustly classify the datasets.On both publicly available and known datasets (MUCT and FaceScrub), the model is assessed to go to perfect accuracy.Tested by comparative experiments with classical machine learning models (Naive Bayes, KNN, Decision Tree and Random Forest), the presented 1D-HD model proves to be more accurate and better at generalization than all other models.The model performs fast inference (<=12 seconds) on large-scale FaceScrub dataset despite a longer training time, which can be used in the real-world domain.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.010 |
| Open science | 0.001 | 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 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".