# Driver Identification and Detection of Drowsiness while Driving
## 6 Results
Hit: Correct recognition of the driving individual by the program. 2. Mis-identifying: Failure of the program to authenticate any stored faces or detection of an incorrect driver. 3. Failure: The program is unable to detect any face, resulting in automatic closure. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Frames per Second (FPS)** & **Image Resolution** & **Result** \\ \hline
30 & 1280 \(\times\) 480 & 22.9 Mbs \\ \hline
15 & 1280 \(\times\) 480 & 10.8 Mbs \\ \hline
5 & 1280 \(\times\) 480 & 3.69 Mbs \\ \hline
30 & 640 \(\times\) 240 & 7.24 Mbs \\ \hline
15 & 640 \(\times\) 240 & 3.3 Mbs \\ \hline
5 & 640 \(\times\) 240 & 1.08 Mbs \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results obtained with liftop. Figure 10: Graphical iftop results. A notable aspect of the tests pertains to the direction of the driver's gaze. Participants were instructed to alternate their gaze between the road and the camera to simulate real driving conditions. An analysis of results reveals variations depending on the driver's gaze direction. When looking at the road, the visibility of the left side of the driver's face may be compromised, significantly impacting facial embedding calculation. However, under ideal conditions with no facial obstructions, a 100% accuracy rate was achieved both when facing the camera directly and when focusing on the road. The second test involved drivers wearing eyeglasses, resulting in a 97.5% recognition rate when facing the camera, with a 2.5% error rate. However, recognition dropped to 88% when drivers looked at the road, as 12% were misidentifying due to factors previously discussed, along with potential reflections in glasses hindering eye detection. The subsequent tests involved drivers wearing sunglasses, yielding favorable results of 85.4% correct identifications and 14.6% wrongly recognized when facing the camera. However, recognition dropped to 49.2% when drivers looked at the road, with 36.2% instances of misidentifying and 14.6% failures, primarily due to obstructed facial features. The tests involving masks showed expected outcomes, with recognition rates significantly lower due to facial coverage. When facing the camera, the recognition rates were 23.6% for hits, 20.7% for wrongly recognized, and 55.7% for misses. When looking at the road, recognition was unsuccessful in 100% of cases due to the combined effects of mask coverage and obscured facial features. Finally, tests involving drivers with long, loose hair showed 100% accuracy when their hair was tied back, demonstrating negligible impact on facial recognition. For the drowsiness phase, algorithm robustness was initially characterized using various videos featuring 15 individuals in different conditions. The tests were conducted with subjects seated in a car, with the camera positioned consistently. Factors such as wearing glasses, masks, hair obstruction, and lighting conditions were considered. The initial challenges included algorithm malfunction under low illumination, difficulty distinguishing closed eyes from half-closed eyes due to light, and misreading eyes during face turns. Notably, the tests did not involve sunglasses, as drowsiness accidents are less likely in bright conditions. Despite these challenges, the detection system yielded 100% accuracy. In order to address these limitations, future iterations could incorporate high-quality infrared or thermal cameras to enhance eye detection under challenging conditions such as low light or when wearing sunglasses.