Adaboost modular tensor locality preservative projection: face detection in video using Adaboost modular‐based tensor locality preservative projections
Bibliographic record
Abstract
Automatic face detection is a challenging task for computer vision and pattern recognition applications such as video surveillance and traffic monitoring. During the last few years, subspace methods have been proposed for visual learning and recognition which are sensitive to variations in illumination, pose and occlusion. To overcome these problems, the authors have proposed a method that combines block‐based tensor locality preservative projection (TLPP) with Adaboost algorithm which improves the accuracy of face detection. In the proposed algorithm Adaboost modular TLPPs (AMTLPPs), the face image is divided into overlapping small blocks and these block features are given to TLPP to extract the features where TLPP take data directly in the form of tensors as input. AMTLPP algorithm selects the optimal block features from the large set of the block features which forms the weak classifiers and are combined to form the strong classifier. A number of assessments are conducted for YouTube celebrity, McGill face dataset and also on collected video sequences of an own dataset recorded under indoor, outdoor, day, sunset and crowded environment. Experimental results show that the proposed approach is effective and efficient.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".