Secure biometric system for accessing home appliances via Internet
Bibliographic record
Abstract
Alongside multitude of innovations and consequential changes in lifestyle, the ability to access home appliances or security cameras over the Internet has been in demand for some years now. In this paper, we propose an efficient, low-cost and user-friendly method to access networked home appliances over the Internet, providing strong, well integrated, three levels of security to the whole application and user data. We present a scheme in which, iris image is used for user authentication and after performing its hashing (using BioHashing), it is sent to the server through a secure communication channel utilizing Secure Socket Layer (SSL). The deterministic feature sequence from the iris image is extracted using 1D log-Gabor filters. Mersenne twister random number generator algorithm is used to produce an array of pseudo-random numbers which is later orthonormalised employing GramSchmidt orthonormalization algorithm for performing BioHashing. In addition to this protected interaction mechanism, we use a single Authentication Server in order to enable access to the home appliances of a complete housing society via Internet, which reduces user responsibility and improves accessibility of this endeavor without compromising on system security. We demonstrate the perfect recognition efficiency of this system with equal error rate (EER) of 0% on CASIA 1 iris image dataset.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".