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Record W2155969220 · doi:10.1109/ratfg.1999.799230

Detection and tracking of faces in real environments

2003· article· en· W2155969220 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVideo Surveillance and Tracking Methods
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaJohns Hopkins University
KeywordsComputer visionComputer scienceArtificial intelligenceFrame rateInterface (matter)Frame (networking)Face (sociological concept)Tracking (education)Active vision

Abstract

fetched live from OpenAlex

A stereo active vision interface is introduced which detects frontal faces in real world environments and performs particular active control tasks dependent on changes in the visual field. Firstly, connected skin colour regions in the visual scene are detected by applying a radial scanline algorithm. Secondly, facial features are searched for in the most salient skin colour region while the blob is tracked by the camera system. The facial features are evaluated and, based on the obtained results and the current state of the system, particular actions are performed. The SAVI system is thought of as a smart laser interface for teleconferencing, telemedicine, and distance learning. The system is designed as a Perception-Action-Cycle (PAC), processing sensory data of different kinds and qualities. Both the vision module and the head motion control module work at frame rate. Hence, the system is able to react instantaneously to changing conditions in the visual scene.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.616
Threshold uncertainty score0.136

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.281
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations22
Published2003
Admission routes2
Has abstractyes

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