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 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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".