Video-Based Discrimination of Genuine and Counterfeit Facial Features Leveraging Cardiac Pulse Rhythm Signals in Access Control Systems
Bibliographic record
Abstract
Facial recognition technology, utilizing short-duration video, offers an accurate access control mechanism, capable of distinguishing genuine faces from fraudulent representations.However, the ability to detect concealed or artificially altered faces, as well as printed spoofs, remains a considerable challenge, even with sophisticated biometric recognition algorithms.This limitation can significantly undermine the performance of access control systems, rendering them susceptible to potential security breaches.In this study, we present an innovative technique that leverages the separation of Red, Green, and Blue (RGB) channels to discern between authentic faces and printed spoofs in color video recordings.This technique is deployed over a 20-second duration to effectively intercept and preclude security violations in access control systems.The proposed method was evaluated with a dataset from 20 participants, demonstrating commendable accuracy in detecting both real and spoof faces.Specifically, the technique exhibited remarkable precision in real face detection and counterfeit face detection.When applied to a 4-second video dataset from the same participants, comparable results were obtained.This method provides a significant advancement in the realm of face-controlled access systems by facilitating precise real-face detection via cost-effective video imaging, predicated on cardiac pulse rhythm signals.This methodology holds the potential to enhance the reliability of facial recognition systems, particularly in high-security environments.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".