Automated Facial Emotion Recognition Using the Pelican Optimization Algorithm with a Deep Convolutional Neural Network

Abstract

Facial emotion recognition (FER) stands as a pivotal artificial intelligence (AI)-driven technology that exploits the capabilities of computer-vision techniques for decoding and comprehending emotional expressions displayed on human faces. With the use of machine-learning (ML) models, specifically deep neural networks (DNN), FER empowers the automatic detection and classification of a broad spectrum of emotions, encompassing surprise, happiness, sadness, anger, and more. Challenges in FER include handling variations in lighting, poses, and facial expressions, as well as ensuring that the model generalizes well to various emotions and populations. This study introduces an automated facial emotion recognition using the pelican optimization algorithm with a deep convolutional neural network (AFER-POADCNN) model. The primary objective of the AFER-POADCNN model lies in the automatic recognition and classification of facial emotions. To accomplish this, the AFER-POADCNN model exploits the median-filtering (MF) approach to remove the noise present in it. Furthermore, the capsule-network (CapsNet) approach can be applied to the feature-extraction process, allowing the model to capture intricate facial expressions and nuances. To optimize the CapsNet model’s performance, hyperparameter tuning is undertaken with the aid of the pelican optimization algorithm (POA). This ensures that the model is finely tuned to detect a wide array of emotions and generalizes effectively across diverse populations and scenarios. Finally, the detection and classification of different kinds of facial emotions take place using a bidirectional long short-term memory (BiLSTM) network. The simulation analysis of the AFER-POADCNN system is tested on a benchmark FER dataset. The comparative result analysis showed the better performance of the AFER-POADCNN algorithm over existing models, with a maximum accuracy of 99.05%.