Abstract: Emotion recognition plays a crucial role in advancing artificial intelligence (AI) systems, enabling more human-like interactions in fields such as mental health, security, customer experience, and human-computer interaction. Traditional methods of emotion detection rely on a single modality, limiting the accuracy and depth of emotional understanding. This study presents a multimodal emotion detection system that integrates image, video, audio, and text analysis using deep learning models. The proposed system leverages Convolutional Neural Networks (CNNs) for facial expression analysis, Long Short-Term Memory (LSTM) for video-based emotion recognition, Mel-Frequency Cepstral Coefficients (MFCCs) with deep learning for speech emotion detection, and Natural Language Processing (NLP) for sentiment analysis in text. The system is deployed as a Flask-based web application, enabling real-time emotion classification. Key challenges such as data privacy, model bias, and real-time efficiency are addressed using ethical AI practices and optimized deep learning architectures. The paper explores the impact of multimodal emotion detection in mental health diagnostics, AI-driven assistants, security systems, and customer engagement platforms, highlighting its potential to enhance machine understanding of human emotions.

Keywords: Multimodal Emotion Detection, Deep Learning,Convolutional Neural Networks ,Facial Expression ,Sentiment Analysis , Speech Emotion Detection

| DOI: 10.17148/IARJSET.2025.12241