Frontal face detection for surveillance purposes using dual Local Binary Patterns features
Bibliographic record
Abstract
Face detection in video sequence is becoming popular in surveillance applications, but the usage of large number of features and the long training time are persistent problems. This paper integrates two types of Local Binary Patterns (LBP) features in order to achieve a high detection rate with a high discriminative power face detector. First LBP feature is a novel way of using the Circular LBP, in which the pixels of the image are targeted; it is a non-computationally expensive feature extraction. The second LBP feature is the LBP Histogram, in which regions in the image are targeted; it is more computationally expensive than Circular LBP features but has higher discriminative power. The proposed detector is examined on real-life low-resolution surveillance sequence. Conducted experiments show that the proposed detector achieves 98% detection rate in comparison to 91% for the Lienhart detector. The proposed detector tolerates wide range of illumination changes.
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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 | 0.000 |
| Open science | 0.000 | 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".