Behavioral Features for Different Haptic-based Biometric Tasks
Bibliographic record
Abstract
The science of haptics or haptic technology has received enormous attention in the last decade for multiple applications. A new promising example is the use of haptic-based systems for individual authentication. A user's behavioral characteristics captured while interacting with a virtual scene are significantly more dfficult to compromise than traditional means (i.e. login ID and passwords). Moreover, another advantage is that this technology allows a user not only to gain access to the system but also to continuously verify individual authenticity during the whole session of haptic interaction. Our current haptic-based biometric system for authentication (the BioHaptic system) has integrated a variety of applications: solving a virtual maze, signing a virtual cheque and dialing phone codes on a virtual phone. In this paper, with the use of a 3D visual representation we study: (1) a subset of features with the greatest user-classificatory worth and (2) whether or not such features are dependent on the application used. We believe this study will enhance the success of individual authentication based on human-haptic interactions.
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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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".