Table 2 Summary of research on facial classification

[65]

Utilizes feature vectors in FaceNet for efficient face recognition, clustering, and verification tasks.

CNN-based Euclidean embedding

Explores facial recognition, presenting solutions to pose and illumination issues, relevant for enhanced biometric systems in autonomous vehicles.

[66]

Presents a multi-resolution cascade network to handle pose, expression, and lighting issues in facial recognition.

CNN Cascade with discriminative capabilities

Introduces a discriminative cascade system for efficient facial distinctions analysis, pertinent to vehicle cabin surveillance.

[67]

Explores appearance-based gaze estimation in non-lab conditions using the MPIIGaze dataset with 213,659 images from 15 participants.

Appearance-based gaze estimation

Investigates gaze estimation under everyday conditions, contributing to improved facial feature recognition within autonomous vehicle cabins.

[68]

Detects driver emotions unobtrusively via smartphone-captured contextual features, outperforming facial recognition by 7 percent and ensuring privacy.

YOLOv5, Microsoft Face Recognition, DeepLabV3, OpenCV

Unobtrusive Sensor Feed Pipeline analyzes driver emotions less intrusively, relevant to vision systems in autonomous vehicle cabins.

[69]

Proposes a hazardous driving image classification system using a modified ShuffleNet model, balancing speed and accuracy for real-time monitoring.

ShuffleNet

Proposes a solution for dangerous driving behavior monitoring, enhancing safety within autonomous vehicle cabins.

[70]

Suggests a deep-learning-based system for drowsiness detection using a novel CNN model for eye state classification, enhancing traveler protection.

CNN - Deep Driver Drowsiness Detector (4D) Model

Uses a deep-learning-based system for drowsiness detection via a novel CNN model, important for driver state analysis in autonomous vehicle cabins.

[71]

Offers a non-invasive approach for driver vigilance classification via deep learning HyMobLSTM (MobileNetV3 and LSTM model) model and transfer learning, analyzing facial and eye components.

HyMobLSTM model ()

Contributes to driver behavior analysis inside autonomous vehicle cabins via a non-invasive vision system, improving safety and alertness monitoring.

[72]

Presents Hypo-Driver, a real-time driver inattention and fatigue detection system using multi-view cameras and biosensors, outperforming existing solutions.

Hypo-Driver system: fused through CNN, RNN-LSTM, and DRNN (Deep Residual Neural Network)

Hypo-Driver system employs multimodal features for driver hypovigilance detection, aligning with vision-based systems in autonomous vehicles for safety enhancement.

[73]

Monitors driver behavior using image processing and computer vision techniques to prevent accidents, promising high accuracy and real-world application potential.

OpenCV, Support Vector Machine (SVM)

Describes real-time driver monitoring using computer vision techniques, relevant for understanding how such techniques can enhance safety and behavior analysis in autonomous vehicle cabins.