Multimodal Emotion Recognition using Convolutional Neural Networks for Advanced Affective Computing and Human-Computer Interaction

Abstract

The precise identification and interpretation of human emotions are essential in the contemporary landscape of affective computing and human-computer interaction. This document outlines an advanced multimodal emotion detection system that integrates the latest techniques in facial expression analysis, speech recognition, and video processing. Traditional methods for emotion identification exhibit limitations, particularly in their ability to accurately capture the intricate and dynamic emotional states of individuals. This study addresses the identified challenges by developing a comprehensive framework that integrates multiple modalities to enhance the accuracy of emotion identification. An extensive analysis of current techniques, including feature-based and rule-based systems, reveals significant drawbacks such as limited scalability and an inability to handle complex emotional expressions. This document introduces a new method utilizing Convolutional Neural Networks (CNNs), motivated by the need for enhanced efficiency and adaptability in emotion recognition systems. Convolutional Neural Networks (CNNs) provide advantages through hierarchical representation and automatic feature learning. This facilitates the extraction of discriminative emotional cues from various modalities, including speech, facial expressions, and video data. The proposed model aims to address the limitations of traditional methods by employing convolutional neural networks (CNNs) to enhance the reliability and accuracy of emotion recognition across diverse environments and situations.Comprehensive testing and evaluation on benchmark datasets demonstrate that our multimodal CNN-based approach is proficient in accurately identifying and classifying a wide range of emotional states. This research contributes to the field of affective computing by providing a scalable, flexible, and high-performance solution for multimodal emotion recognition, with potential applications in virtual reality, human-computer interaction, and mental health monitoring, among other areas.