Face Detection Using Skin Color Recursive Clustering and Recognition Using Multilinear PCA
Bibliographic record
Abstract
In this paper, we present a robust approach for face recognition from video sequences. An automatic face detectoris employed which uses modified skin color modeling to detect human skin regions from the video sequences. The presence or absence of face in each region is verified by means of height width proportion and a Neural Network based template matching scheme. The obtained face images are then projected onto a feature space, defined by Multilinear Principal Component Analysis (MPCA), to produce the biometric feature template. Recognition is performed by projecting anew image onto the feature spaces by the MPCA that generalizes not only the classical PCA solution but also a number of the so-called 2-D PCA algorithms and then classifying the face by comparing its position in the feature spaces with the positions of known individuals. The proposed method is applicable to security systems, secure human computer interaction, visual communication systems (secure video conferencing) and virtual world environments.
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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 | 0.001 |
| 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".