Abstract

Among the most prominent field in the human-computer interface (HCI) is emotion recognition using facial expressions. Posed variations, facial accessories, and non-uniform illuminations are some of the difficulties in the emotion recognition field. Emotion detection with the help of traditional methods has the shortcoming of mutual optimization of feature extraction and classification. Computer vision (CV) technology improves HCI by visualizing the natural world in a digital platform like the human brain. In CV technique, advances in machine learning and artificial intelligence result in further enhancements and changes, which ensures an improved and more stable visualization. This study develops a new Modified Earthworm Optimization with Deep Learning Assisted Emotion Recognition (MEWODL-ER) for HCI applications. The presented MEWODL-ER technique intends to categorize different kinds of emotions that exist in the HCI applications. To do so, the presented MEWODL-ER technique employs the GoogleNet model to extract feature vectors and the hyperparameter tuning process is performed via the MEWO algorithm. The design of automated hyperparameter adjustment using the MEWO algorithm helps in attaining an improved emotion recognition process. Finally, the quantum autoencoder (QAE) model is implemented for the identification and classification of emotions related to the HCI applications. To exhibit the enhanced recognition results of the MEWODL-ER approach, a wide-ranging simulation analysis is performed. The experimental values indicated that the MEWODL-ER technique accomplishes promising performance over other models with maximum accuracy of 98.91%.

Introduction

Since people have become more informed, they need a high level of computer intelligence [1], [2]. Human-computer interaction (HCI) is not limited to original hardware-related communication. Certain smarter communication techniques are appearing gradually in the life of people like a sequence of more intellectual techniques relevant to voice recognition [3], face recognition, and gesture recognition. Intellectual mechanisms can help establish interactions between computers and humans [4], [5]. The advent of more convenient communication techniques is becoming a major advancement trend in the current domain of HCI. The objective of HCI development was naturally to make computers adapt and serve the requirements of individuals [6].

People-centred instead of compelling persons to adapt to computers. Emotions had a main role during the interaction. Detection of facial emotions will be helpful in several tasks like social robots, criminal justice systems, security monitoring, customer satisfaction identification, smart card applications, e-learning, etc [7], [8]. The core blocks in the conventional emotion recognition mechanism were classifying the emotions, detecting faces, and extracting the features [9].

Conclusion

In this study, a novel MEWODL-ER method was introduced for emotion classification in the HCI applications. The presented MEWODL-ER algorithm is intended for the identification of various types of emotions that exist in the HCI applications. The proposed model follows a three stage process namely GoogleNet feature extraction, MEWO based hyperparameter tuning, and QAE classification. The design of automated hyperparameter adjustment using the MEWO algorithm helps in attaining an improved emotion recognition process. To show the enhanced recognition results of the MEWODL-ER approach, a wide-ranging simulation analysis is done. The experimental values indicated that the MEWODL-ER technique accomplishes promising performance over other models with higher accuracy of 98.91%. In the future, the efficiency of the MEWODL-ER algorithm will be boosted by hybrid DL classifiers.