Automated Facial Emotion Detection using Arithmetic Optimization Algorithm with Deep Convolutional Neural Network for Autonomous Vehicle Drivers

Facial Emotion Recognition (FER) utilizing Deep Learning (DL) is indeed employed in the context of autonomous vehicle drivers. By investigating the facial expressions of drivers, the vehicle system is gain insights into the emotional state of drivers. FER is given to whole safety by identifying critical emotions like fear, anger, or surprise. When the system identifies these emotions, it responds accordingly by altering the driving performance or taking preventive measures for avoiding potential risks or aggressive maneuvers. The training of the DL algorithms namely Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are done on the preprocessed data. These approaches learn to extract appropriate features in facial images and forecast the same emotions. This manuscript offers the design of Automated Facial Emotion Detection using Arithmetic Optimization Algorithm with Deep Convolutional Neural Network (AFED-AOADCNN) technique for Autonomous Vehicle Drivers. The purpose of the AFEDAOADCNN technique is to detect various kinds of facial emotions in autonomous vehicle drivers. In this introduced AFED-AOADCNN technique, DCNN method is applied for procedure of feature extraction. Next, the AOA is employed for optimum hyperparameter tuning of the DCNN method. At last, quantized neural network (QNN) approach is exploited for the identification and classification of different kinds of facial emotions. The investigational evaluation of the AFEDAOADCNN method is assessed on facial emotion dataset. The experimental validation stated the improved outputs of the AFED-AOADCNN method over recent approaches.