Bioinspired Image Processing Enabled Facial Emotion Recognition Using Equilibrium Optimizer With a Hybrid Deep Learning Model

Abstract

Owing to the unpredictable nature of human facial expression, Facial emotion recognition (FER) from facial images becomes a tedious process. FER is a field within artificial intelligence (AI) and computer vision (CV) that concentrates on developing algorithms and technologies to interpret and analyze emotional expressions shown on human faces. The major intention of this area of research is to enable computers and machines to automatically classify and detect emotions, including sadness, happiness, anger, etc., based on facial expressions and cues. Deep learning (DL) systems namely recurrent neural network (RNN) and convolutional neural network (CNN) can be used for the task of automatically classifying and detecting emotions from human facial expressions. Furthermore, Bioinspired Image Processing Enabled FER combines principles from biological systems with image processing techniques to better the robustness and accuracy of FER. This technique is inspired by natural processes to enhance the interpretation and understanding of human emotion conveyed by facial expressions. This study designs a new Bioinspired Image Processing Enabled Facial Emotion Recognition using an Equilibrium Optimizer with Hybrid Deep Learning (BIPFER-EOHDL) model. The primary objectives of the BIPFER-EOHDL technique lie in the effectual and automated identification of facial expressions using a hyperparameter-tuned DL algorithm. In the presented BIPFER-EOHDL technique, the median filtering (MF) approach can be applied for image pre-processing. Besides, the BIPFER-EOHDL method applies the EfficientNetB7 model for the process of feature extraction. Meanwhile, the hyperparameter selection of the EfficientNetB7 model takes place by the use of the EO algorithm. Finally, the multi-head attention bi-directional long short-term memory (MA-BLSTM) model is exploited for the recognition and classification of facial emotions. A wide-ranging simulation analysis was performed to validate the higher FER outcomes of the BIPFER-EOHDL methodology. The simulation results stated that the BIPFER-EOHDL technique accomplishes better FER results than other recent approaches.

Keywords

• Non-verbal communication
• facial emotion recognition
• deep learning
• equilibrium optimizer
• artificial intelligence