Driver Identification System Using Finger Vein and YOLO Object Detection
Bibliographic record
Abstract
Biometrics used to identify people is one of the safest and most convenient identification methods. The hypoxic hemoglobin method can present a human finger vein image for driver identification through infrared ray irradiation. The finger vein image is captured system; with the finger vein image processed using contrast-limited adaptive histogram equalization (CLAHE) and the Gabor filter to obtain a clearer image. The YOLO object detection technology is used to drive identification. The proposed system can be divided into two parts. The first part is the training part, which processes images. A database generates the weight files for external testing. The second part is the testing system. When the driver's finger is placed in the designated photo area a photo will be taken. The photo will be processed and run on Raspberry Pi 4 together with the weight file to identify the driver. The experimental results show that when the database has a large amount of data, the yolov4-tiny-hy recognition rate is comparable to that of YOLOv4. The training time is greatly shortened.
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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 | 0.001 | 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".