MétaCan
Menu
Back to cohort
Record W2046441934 · doi:10.4236/jis.2012.32009

Determinants in Human Gait Recognition

2012· article· en· W2046441934 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.

Bibliographic record

VenueJournal of Information Security · 2012
Typearticle
Languageen
FieldEngineering
TopicGait Recognition and Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceGaitHuman bodyCenter of gravitySet (abstract data type)Artificial intelligenceComputer visionPoint (geometry)Human motionMotion (physics)Pattern recognition (psychology)Physical medicine and rehabilitation

Abstract

fetched live from OpenAlex

Human gait is a complex phenomenon involving the motion of various parts of the body simultaneously in a 3 dimensional space. Dynamics of different parts of the body translate its center of gravity from one point to another in the most efficient way. Body dynamics as well as static parameters of different body parts contribute to gait recognition. Studies have been performed to assess the discriminatory power of static and dynamic features. The current research literature, however, lacks the work on the comparative significance of dynamic features from different parts of the body. This paper sheds some light on the recognition performance of dynamic features extracted from different parts of human body in an appearance based set up.

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 score0.647
Threshold uncertainty score0.226

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 communication0.0000.002
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 head0.013
GPT teacher head0.245
Teacher spread0.231 · 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