Multimodal Emotion Recognition System Using Deep Learning and Decision Fusion Techniques

Abstract

Understanding human emotions is a fundamental aspect of effective human-computer
interaction. With the rapid advancement of artificial intelligence, emotion recognition
systems have gained significant importance in areas such as healthcare, education,
security, and customer experience. This research presents a multimodal emotion
recognition system that integrates facial expressions, textual input, and audio signals
using deep learning and decision fusion techniques.The proposed system leverages three
independent models: a facial emotion recognition model, a textual sentiment analysis
model, and an acoustic emotion detection model. Each model processes a specific
modality and generates probability scores for different emotional states such as happiness,
sadness, anger, and neutrality. The facial model analyzes real-time webcam frames, the
text model processes user-provided textual input, and the audio model captures emotional
cues from speech signals.
To enhance prediction accuracy and robustness, a decision fusion engine is implemented.
This engine combines the outputs from individual modalities using a fusion strategy to
determine the final emotional state. By integrating multiple sources of information, the
system overcomes limitations associated with unimodal approaches, such as noise,
ambiguity, and incomplete data.The system is implemented using the Django framework,
enabling real-time interaction through a web-based interface. User inputs are captured,
processed, and analyzed, and the results are displayed dynamically. The system also
stores emotion logs in a database, allowing historical analysis and pattern recognition.