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Record W2015549092 · doi:10.1080/18756891.2011.9727894

Edge Eigenface Weighted Hausdorff Distance for Face Recognition

2011· article· en· W2015549092 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

VenueInternational Journal of Computational Intelligence Systems · 2011
Typearticle
Languageen
FieldComputer Science
TopicFace and Expression Recognition
Canadian institutionsMinistry of Transportation of Ontario
FundersBeijing Institute of TechnologyNational Natural Science Foundation of ChinaYale University
KeywordsDiscriminative modelEigenfacePattern recognition (psychology)Facial recognition systemArtificial intelligenceHausdorff distanceFace (sociological concept)WeightingComputer scienceEnhanced Data Rates for GSM EvolutionHausdorff spaceMathematicsCombinatoricsMedicine

Abstract

fetched live from OpenAlex

The different face regions have different degrees of importance for face recognition. In previous Hausdorff distance (HD) measures, points are treated as same importance, or weight different points that calculated from gray domain. In this paper, a new weighting function of HD based on the eigenface from edge domain, which reflects the discriminative properties of face edge images effectively, is proposed for face recognition. Experiments show the proposed method outperforms previous HD measures.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.887
Threshold uncertainty score0.699

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.071
GPT teacher head0.306
Teacher spread0.235 · 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