Secure and simplified access to home appliances using Iris recognition
Bibliographic record
Abstract
Moving towards an dasiaalways-onpsila, dasiamobilepsila and technology driven lifestyle, people are demanding greater technical triumph to make life more exciting, convenient and trouble-free. Automation at home has already started catering to this growing need. Another major motivating factor for this is the prospect of higher energy efficiency, greater control on home from remote locations and the decreasing cost of network-controlled home appliances. In this paper, we propose a novel scheme for accessing home appliances over the Internet using secure communication channel offered by secure socket layer (SSL) with mandatory certificate verification, perform user authentication using iris image, and then hash the biometric data to provide an impregnable three-factor security to the biometric data as well as user instructions while in transit between the user terminal and the home appliance. Additionally, we propose the use of a single authentication server for multiple residences, that would store users' sensitive biometric data, perform authentication, access control and quality of service, thereby reducing cost, effort, user dependability and improve security, acceptability and user-friendliness of home automation universally, together with SIP and UPnP protocols. The proposed approach proves to be more effective because of its well integrated three levels of security, less equal error, very small hash code used for authentication, lower computational complexity while matching, and thus, very fast response.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".