Multi-Modal Emotion Detection System

Abstract

The Multi-Modal Emotion Detection System combines real-time facial expression recognition, audio-based emotion classification, and hardware-level noise filtering to provide accurate and reliable emotion analysis in varying environments. It captures live video and audio, enhances audio clarity through a noise-suppression module, and uses deep learning models to classify emotions from both modalities. A fusion mechanism integrates facial and audio results for higher accuracy, while all processed emotion data is securely transmitted to a backend server. A web dashboard allows users and administrators to view real-time emotion states, trends, and analytics. The system also monitors input quality and alerts users when facial visibility or audio clarity is disrupted. By integrating multi-modal sensing, noise filtering, and automated backend processing, the solution ensures consistent emotion detection and supports applications in monitoring, healthcare, education, and human–computer interaction.

Introduction

The project presents a Multi-Modal Emotion Detection System designed to overcome the limitations of traditional facial-only or audio-only emotion recognition, which often fail in real-world environments affected by noise, poor lighting, or user movement. The proposed system integrates live facial analysis, audio-based emotion recognition, and custom hardware-level noise filtering, enabling highly accurate, stable, and low-latency emotion detection across dynamic conditions. Using deep learning models, synchronized video and audio streams are processed in real time, fused for improved reliability, and visualized on an interactive cloud-connected web dashboard.

The project aims to:

1. Capture real-time multi-person video and audio;

2. Provide a comprehensive web dashboard with analytics and export features;

3. Integrate custom noise-filtering hardware to enhance audio clarity and achieve sub-200ms latency;

4. Enable individual tracking with timestamps for longitudinal emotion analysis.

Existing systems rely on either facial or audio inputs and often fail under environmental disturbances. Even multimodal systems lack hardware-level noise suppression, rely heavily on cloud services, and rarely offer interactive real-time dashboards. These limitations reduce reliability and restrict practical use in fields such as mental-health assessment, education, or surveillance.

The proposed solution addresses these issues through a hardware-assisted, multi-sensor architecture combining CNN/Transformer-based facial emotion recognition, MFCC/LSTM or Transformer-based audio emotion detection, and a hybrid fusion mechanism that adapts to environmental conditions. A secure backend handles inference, data storage, user profiles, and system communication, while a responsive dashboard displays live emotion states, trends, historical analytics, and multi-user monitoring.

The methodology includes modular development of hardware, audio-visual data processing, real-time detection pipelines, and server–dashboard integration. Facial emotions are detected via YOLOv8-Face/RetinaFace and hybrid deep networks, while audio is enhanced with physical noise filtering followed by digital processing (VAD, spectral subtraction). Processed outputs are fused and transmitted to the dashboard.

Implementation spans the frontend dashboard, backend APIs, noise-filtering hardware, computer-vision pipelines, audio processing, behavior monitoring, and structured database storage. High-resolution cameras and directional microphones feed data into a synchronized processing system designed for scalability and low latency.

Experimental results across varied conditions—noise, lighting changes, multiple speakers, and movement—show that the multimodal system is highly accurate, stable, and context-aware. Hardware-level filtering notably improves audio-based emotion detection, while fusion mechanisms enhance reliability when one modality becomes weak. The dashboard provides smooth, real-time visualization and analytics, demonstrating the system’s readiness for deployment in complex real-world scenarios.

Conclusion

In this work, we presented a Multi-Modal Emotion Detection System that integrates synchronized facial analysis, speech-based emotion recognition, and hardware-supported noise filtering to enable reliable real-time affect estimation. The proposed architecture combines computer vision, acoustic feature extraction, and deep-learning–driven inference modules with a cloud-connected dashboard for continuous monitoring and visual analytics. Experimental evaluations demonstrate that the multimodal fusion strategy substantially improves robustness under challenging environmental conditions, including variable lighting, background noise, and multi-user scenarios. The system’s ability to dynamically adjust modality weights further enhances prediction stability and reduces error rates compared to single-modality baselines. The lightweight database design and modular backend allow scalable deployment across diverse application domains, such as education, healthcare, workplace assessment, and human–machine interaction. Future work will focus on expanding cross-cultural emotion datasets, integrating physiological sensors, and optimizing inference for edge-computing platforms to further enhance adaptability and real-world performance.

References

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Copyright

Copyright © 2025 Priya V, Lavanya , Deekshitha E T, Chinmayi H, Sahana M A. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.