A Novel Biometric System for Identification and Verification of Haptic Users
Bibliographic record
Abstract
Currently, almost all systems involve an identity authentication process before a user can access requested services such as online transactions, entrance to a secured vault, logging into a computer system, accessing laptops, secure access to buildings, etc. Therefore, authentication has become the core of any secure system, wherein most of the cases rely on identity recognition approaches. Biometric systems provide the solution to ensure that the rendered services are accessed only by a legitimate user and no one else. Biometric systems identify users based on behavioral or physiological characteristics. The advantages of such systems over traditional authentication methods, such as passwords and IDs, are well known; hence, biometric systems are gradually gaining ground in terms of usage. We investigate the issues related to the usage of haptics as a mechanism to extract behavioral features that define a biometric identifier system. In order to test this possibility, we design a haptic system in which position, velocity, force, and torque data from the instrument is continuously measured and stored as users perform a specific task. We analyze the information content of the haptic data generated directly from the instrument's interface. We then measure the physical attributes such as force and torque that provide the richest information content pertaining to a user's identity. Through a series of experimental work, we discover that haptic interfaces are more suited to verification mode rather than identification mode. Finally, we implement a biometric system based on haptics
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".