Robust Facial Expression Recognition Using an Evolutionary Algorithm with a Deep Learning Model

The most important component that can express a person's mental condition is facial expressions. A human can communicate around 55% of information non-verbally and the remaining 45% audibly. Automatic facial expression recognition (FER) has now become a challenging task in the surveying of computers. Applications of FER include understanding the behavior of humans and monitoring moods and psychological states. It even penetrates other domains-namely, robotics, criminology, smart healthcare systems, entertainment, security systems, holographic images, stress detection, and education. This study introduces a novel Robust Facial Expression Recognition using an Evolutionary Algorithm with Deep Learning (RFER-EADL) model. RFER-EADL aims to determine various kinds of emotions using computer vision and DL models. Primarily, RFER-EADL performs histogram equalization to normalize the intensity and contrast levels of the images of identical persons and expressions. Next, the deep convolutional neural network-based densely connected network (DenseNet-169) model is exploited with the chimp optimization algorithm (COA) as a hyperparameter-tuning approach. Finally, teaching and learning-based optimization (TLBO) with a long short-term memory (LSTM) model is employed for expression recognition and classification. The designs of COA and TLBO algorithms aided in the optimal parameter selection of the DenseNet and LSTM models, respectively. A brief simulation analysis of the benchmark dataset portrays the greater performance of the RFER-EADL model compared to other approaches.