A Survey of Biometric Authentication Technologies Towards Secure And Robust Systems: A Case Study of Mount Kenya University
Bibliographic record
Abstract
In response to the increased demand for more effective authentication methods, the usage of biometric authentication to secure systems against unwanted access has grown. Because of the recent COVID-19 pandemic outbreak, any direct physical contact with the system should be avoided. Furthermore, current authentication systems lack the necessary security features, making them vulnerable to cyber risks such as forgery by unethical employees and unauthorized users. The goal of this paper is to investigate the existing biometric authentication systems and propose the best security models to overcome the weaknesses of existing technologies. The study employed mixed methodology, which was qualitative and quantitative in nature and relied on primary and secondary sources of data. The researcher collected the data from a population of 300 staff of Mount Kenya University with a sample size of 169 respondents. The R2 value on the relationship between the studied dependent and independent variables was R2 = 0.792 showing a good fit of the model since is greater than 50% of the test item used in the case study. Therefore the study recommends that institutions to implement a contactless biometric system to eliminate physical contact and use multimodal system that will help overcome the existing challenges associated with unimodal systems. There are still gaps for future researchers where they need to focus on the various decision algorithms that are best efficient in verifying users before they are authenticated in the system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".