Personalized Emotion Recognition and Emotion Prediction System Based on Cloud Computing

Wenqiang Tian

doi:10.1155/2021/9948733

Abstract

Promoting economic development and improving people’s quality of life have a lot to do with the continuous improvement of cloud computing technology and the rapid expansion of applications. Emotions play an important role in all aspects of human life. It is difficult to avoid the influence of inner emotions in people’s behavior and deduction. This article mainly studies the personalized emotion recognition and emotion prediction system based on cloud computing. This paper proposes a method of intelligently identifying users’ emotional states through the use of cloud computing. First, an emotional induction experiment is designed to induce the testers’ positive, neutral, and negative three basic emotional states and collect cloud data and EEG under different emotional states. Then, the cloud data is processed and analyzed to extract emotional features. After that, this paper constructs a facial emotion prediction system based on cloud computing data model, which consists of face detection and facial emotion recognition. The system uses the SVM algorithm for face detection, uses the temporal feature algorithm for facial emotion analysis, and finally uses the classification method of machine learning to classify emotions, so as to realize the purpose of identifying the user’s emotional state through cloud computing technology. Experimental data shows that the EEG signal emotion recognition method based on time domain features performs best has better generalization ability and is improved by 6.3% on the basis of traditional methods. The experimental results show that the personalized emotion recognition method based on cloud computing is more effective than traditional methods.

Highlights

Emotion recognition has become the research field of artificial intelligence [1]
In the research of emotion recognition, many aspects are included, such as the recognition of facial emotions, the recognition of sound emotions, the recognition of body emotions, and the recognition of physiological signal emotions. e communication between people is to convey information through language, and the emotional information conveyed through sound signals is an important source of information and an indispensable part of people’s perception and judgment of things
The EEG signal emotion recognition method based on time domain features performs best and has better generalization ability. It is improved by 6.3% on the basis of traditional methods. is shows that EEG data samples will be in the new feature space

Summary

Introduction

Emotion recognition has become the research field of artificial intelligence [1]. With the improvement of cloud computing technology’s ability to perceive human emotions, the interaction between humans and computers has been improved, especially in human-computer interaction, virtual reality, and computer-assisted application in education. Jenke R uses EEG signals for emotion recognition, which can directly assess the user’s “internal” state, which is considered an important factor in human-computer interaction [7]. He has studied many feature extraction methods and usually selects suitable features and electrode positions based on neuroscience findings. Use deep neural network (DNN) or support vector machine (SVM) to obtain the latest results of auditory and visual emotion recognition He proposed that the ELM paradigm is a fast and accurate alternative to these two popular machine learning methods. Zheng W L introduced the Deep Belief Network (DBN) to build an EEG-based emotion recognition model for three emotions: positive, neutral, and negative He developed an EEG data set obtained from 15 subjects. The correct emotion recognition rate in different states can be calculated

Personalized Emotion Recognition Based on Cloud Computing

Facial Emotion Recognition Experiment Based on Cloud Computing

Findings

Analysis of Facial Emotion Recognition Data