Iris detection for gaze tracking using video frames
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
The computation of gaze direction is important in modern interactive systems. The displays in real-time monitoring systems depend on spatial and temporal characteristics of eye movement. Research studies indicate the requirement for efficient and novel techniques in human computer interaction. A strong need for gaze tracking methods that eliminate initial setup and attune procedure is required. The pupil, iris and eye corners provide parametric data to determine gaze direction. Gaze tracking algorithm is initiated by iris localization. The approach of iris detection using frames captured from the video is significant for feature based gaze tracking. In this paper, the procedures for face and eye detection in visible light are discussed. The novel method discussed in this paper identify single face image appropriate for gaze tracking by elimination of multiple and non-face images. Iris detection is performed using Hough gradient method. The correctness rate of iris detection obtained is 95%.
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 | 0.000 | 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 | 0.000 | 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 it