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