Marine predators optimization with deep learning model for video-based facial expression recognition

Video-based facial expression recognition (VFER) technique intends to categorize an input video into different kinds of emotions. It remains a challenging issue because of the gap between visual features and emotions, problems in handling the delicate movement of muscles, and restricted datasets. One of the effective solutions to solve this problem is the exploitation of efficient features defining facial expressions to carry out FER. Generally, the VFER find useful in several areas like unmanned driving, venue management, urban safety management, and senseless attendance. Recent advances in computer vision and deep learning (DL) techniques enable the design of automated VFER models. In this aspect, this study establishes a new Marine Predators Optimization with Deep Learning Model for Video-based Facial Expression Recognition (MPODL-VFER) technique. The presented MPODL-VFER technique mainly aims to classify different kinds of facial emotions in the video. To accomplish this, the presented MPODL-VFER technique derives features using the deep convolutional neural network based densely connected network (DenseNet) model. The presented MPODL-VFER technique employs MPO technique for the hyperparameter adjustment of the DenseNet model. Finally, Elman Neural Network (ENN) model is exploited for emotion recognition purposes. For assuring the enhanced recognition performance of the MPODL-VFER approach, a comparison study was developed on benchmark dataset. The comprehensive results have shown the significant outcome of MPODL-VFER model over other approaches.