A Novel Deep Learning-Based Bidirectional Elman Neural Network for Facial Emotion Recognition

Facial emotion recognition (FER) is an interesting area of research. It has a wide range of applications, but there is still a deficiency of an accurate approach to provide better results. A novel FER system to maximize classification accuracy has been introduced in this paper. The proposed approach constitutes the following phases: pre-processing, feature extraction, feature selection, and classification. Initially, the images are pre-processed using the extended cascaded filter (ECF) and then the geometric and appearance-based features are extracted. An enhanced battle royale optimization (EBRO) for feature selection has been proposed to select the relevant features and to reduce the dimensionality problem. Then, the classification is carried out using a novel bidirectional Elman neural network (Bi-ENN) that offers high classification results. The proposed Bi-ENN-based emotion classification can accurately discriminate the input features. It enabled the model to predict the labels for classification accurately. The proposed model on evaluations attained an accuracy rate of 98.57% on JAFFE and 98.75% on CK+ datasets.

Automated summary

This paper introduces a novel facial emotion recognition (FER) system that maximizes classification accuracy. The system consists of phases including pre-processing, feature extraction, feature selection, and classification. It utilizes extended cascaded filter for image pre-processing, extracts geometric and appearance-based features, employs enhanced battle royale optimization for feature selection, and uses bidirectional Elman neural network for emotion classification. The proposed system achieved high accuracy rates in evaluations.