Biometric ontology for semantic biometric‐as‐a‐service (BaaS) applications: a border security use case
Bibliographic record
Abstract
With the fast adoption of cloud computing, the use of biometric technologies has evolved to adifferent way of providing security, preserving privacy, and analysing personaltraits for various purposes. The main components of any biometric system, suchas biometric sensing, data gathering, feature extraction, identification,verification, recognition, and analytics, are now handled over distributednetworks. Many of the biometric system services are presented over such networkswhich are followed by the creation of a new concept ‘biometric‐as‐a‐service(BaaS)’. Recent BaaS approaches usually focus on identifying the effectivedistributed architectures, policies, and use case recommendations. However,there is a strong need to focus on developing a semantic framework which shouldrely on a biometric ontology. This study presents such an ontology covering theuses of different biometric modalities, evaluation and assessment of biometricsystems, modelling biometric processes, and analyses through interlinkedrelations with biometric stakeholders. In order to shed light on how such anontology is useful for BaaS solutions, a case study focusing on the various usesof biometric modalities is presented. The selected use case addresses the asylumseeker or immigrant identification problems regarding the border securitychallenges where facial biometrics are benefited.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".