An Efficient Emotion Recognition Model Using Bald Hawk Optimization Based on Deep Learning

Abstract

Emotion recognition models are used to determine the thoughts, feelings, and emotions of humans from facial visuals. The enormity of facial expressions makes it challenging to extract emotions from face images. The main focus of this research is to extract emotions from facial images and emotional speech using deep learning models. In previous research, proposed methods suffer from issues like performance degradation caused by poor layer selection as well as poor accuracy. In the proposed model, data is gathered, and preprocessed to improve the image's quality for more accurate emotion recognition. The region extraction is carried out using a faster Recurrent-convolutional neural network (R-CNN) and the standard Resnet-101. Then, a pretrained model is created using the standard combination of the ResNet-101 and GoogLeNet model for feature extraction. To classify emotions accurately, an activational attention layer coupled deep learning model (ALNN-EmR model) is proposed using the bald hawks-based deep convolutional neural network (bald hawks-deep CNN) in this research. In the proposed model, the features are acquired using the ResLeNet model designed by concatenating the ResNet-101 and GoogLeNet features. Using the ResLeNet features, the proposed activational attention layer coupled deep learning model (ALNN-EmR) recognizes the emotions, where the weights and biases of the model are successfully adjusted using the bald hawk optimization (BHO). The proposed ALNN-EmR model is implemented and the effectiveness is revealed through the emotional speech and video-based data analysis.