Feedback or gossip.
An emotion recognition system that analyzes facial expressions and audio signals to infer human mood in real time.
Currently in the initial setup phase, with core research and problem formulation completed. System design and documentation are in progress.
This project focuses on building a multimodal system that recognizes human emotions by analyzing both visual cues and audio signals. It processes facial expressions, micro-movements, and vocal characteristics such as tone, pitch, and intensity to infer emotional states. By combining visual and auditory modalities, the model aims to improve accuracy and contextual understanding compared to single-input systems.
To build a multimodal emotion recognition system that analyzes visual and audio cues for accurate emotional understanding.
Designed to adapt across varied lighting, background noise, and real-world conditions for reliable performance.
Covers facial expression analysis, vocal feature extraction, and multimodal fusion for a defined set of emotions.
Built using established deep learning techniques and publicly available datasets within realistic computational limits.
Modular design allows expansion to larger datasets, additional emotion classes, and real-time inference.
Structured for future integration with interactive systems and additional data modalities.