[4] categorized these into four categories: subjective, psychological, vehicular, and visual. A study has also attempted to use a hybrid approach of the four categories [31]. Among these, visual techniques have emerged as non-intrusive, have proven hardware, and are user-friendly, although they struggle with certain lighting conditions and backgrounds. As a solution, hybrid computer vision techniques stand out, offering both accuracy and non-intrusiveness. [PERSON]. [15] showcased a model leveraging CNN, specifically a D2 CNN-FLD model, centered on facial landmark detection that was trained using the National Tsing Hua University Driver Drowsiness Detection Dataset (NTHU-DDD), and was able to achieve over 83% accuracy. However, poor lighting conditions remained an issue that affected the model's performance [15]. [PERSON] et al. [16] ushered in an innovative cloud-based processing approach. Here, the data underwent a sequence of operations: face tracking, recognition of facial regions, and classification of the driver's fatigue state. This model was then trained using the CelebA and YawDD datasets, which achieved an accuracy of 92% [16]. [PERSON]. [14] used a depth-cascading Multitasking CNN (MTCNN) for face detection to use the facial features for an EM-CNN to analyze the states of the eyes and mouth, which was able to achieve an accuracy of 93.623% by using [PERSON]'s dataset, containing 4000 driver images [14]. Furthermore, research by [PERSON]. [17] used the Circular Hough Transform after face detection with Viola-Jones to obtain PERCLOS [17]. It shows promising results; however, it struggles when faces are turned sideways owing to the constraints of the Viola-Jones method [32]. In addition, [PERSON]. [2] obtained a 96.42% accuracy using Deep Learning with the Viola-Jones method followed by a stacked Deep Convolutional Neural Network (DCNN). This approach was trained using a proprietary dataset encompassing 2850 images [2]. Other techniques, such as those in Ref. [11], used a DCNN along with a Random Forest classifier on the iBUG 300 W dataset and achieved an accuracy of 99%. Furthermore, Refs. [18; 19] proved its beneficiality in a resource-constrained context using heuristic-based eye region positioning and pixel-level classification techniques, respectively [18]. Using the NTHU-DDD Video Dataset and lightweight U-net architecture, the latter obtained an accuracy of 96.72% [19]. In fact, Ref. [20] performed fatigue detection using a visual characteristic analysis using yawn, sleepiness, and head pose. In addition, [PERSON]. [21] and [33] suggested methods for using eye mechanics, analyzing the binary conversion of images [21] and pupillary light reflexes [33]. Finally, Refs. [12; 22] provided fresh perspectives with a temporal network that achieved 84.82% accuracy [12] and a Residual Channel Attention Network (RCAN) that successfully distinguished eye and mouth states with an accuracy of approximately 98.5% [22]. Drowsiness detection technologies have evolved significantly, but they continue to face challenges such as variable lighting conditions, complex backgrounds, and limitations in detecting extreme head poses owing to reliance on traditional methods such as the Viola-Jones face detection method. More recent studies, such as those employing HOG descriptors and PCA by Ref. [23], which achieved 90% accuracy, and Ref. [24], reaching 85.62% accuracy with improved HOG features for a Naive Bayes classifier, indicate ongoing improvements. Furthermore, Ref. [25] utilized an ensemble deep learning architecture with InceptionV3 modules to achieve impressive accuracies of 99.65% (training) and 97.1% (testing). Despite these advancements, there remains a need for an effective solution for challenging detection conditions. This study aims to improve the issues from extreme head poses as well as efficiently perform drowsiness detection to be implemented on low computational devices such as a Jetson Nano (NVIDIA, Santa Clara, CA, USA).