Advancements in Biometric Authentication Systems: A Comprehensive Survey on Internal Traits, Multimodal Systems, and Vein Pattern Biometrics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Biometric authentication systems, entities that leverage unique biological traits for individual identification, have become increasingly relevant in the digital age, addressing critical safety and security concerns.These biometric identifiers, being distinct and irreversible, uniquely differentiate individuals.Biometric recognition's significance extends to diverse domains, including forensics, defense, surveillance, personal identification, and banking.The impetus for advancements in biometric authentication systems is driven by the imperative need for resilience, high precision, and resistance against spoofing.This paper aims to elucidate the recent advancements in this evolving field.The fundamentals of biometric authentication systems, issues and vulnerabilities inherent in basic biometric systems, as well as the cutting-edge biometric systems developed in recent years, are thoroughly reviewed.The paper further explores how challenges can be mitigated through the deployment of Multimodal biometric systems and vein pattern-based systems.A synopsis of real-time face recognition incorporating morphing attack detection is also provided.This comprehensive survey concludes that the performance of biometric recognition systems is continually being augmented, predominantly through the incorporation of deep learning frameworks and 3D biometric imagery, which offer highly accurate representations of human biometric features.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.017 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it