Facial Recognition and Tracking using the Eigenface Technique
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Eigenfaces is a computer vision technique developed in 1991 by M. Turk and A.Pentland used to distinguish an image of a face with only a single 2D image. The purpose of this project was to develop a system capable of automatically recognizing and tracking a face throughout a real-time video using the Eigenfaces technique. These techniques take advantage of the assumptions that faces share relatively the same features and are usually upright. The Eigenfaces technique transforms several images of individual faces from an image into vectors based on the pixel values contained within the image. The vectors are then used to create an n-dimensional space in which future images can be placed to determine the likelihood the image contains a face. This project segments a single image into a grid and applies the Eigenface technique to each segment rather than the entire image. The result of this process is the successful application to each image in a video to track the movement of a face throughout the video in real time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it