Multi-modal Emotion Recognition

Recognition of human emotions through multiple modalities

Overview

In this project, we are developing a multi-modal emotion recognition system that goes beyond traditional methods by incorporating multiple sources of input, such as facial expressions, voice tonality, and text analysis. The goal is to create a comprehensive and accurate model for understanding and interpreting human emotions in various contexts.

Multi-modal emotion recognition UI

Features

  • Facial Expression Analysis:
    • Detect and analyze facial expressions using computer vision techniques.
    • Recognize key facial landmarks and expressions associated with different emotions.
  • Audio Features Analysis:
    • Utilize audio processing to capture and analyze the tonal variations in speech.
    • Identify patterns in pitch, intensity, and tempo to infer emotional states.
  • Textual Data Analysis:
    • Process extracted text to infer emotional cues.
  • Multi-Modal Fusion:
    • Integrate information from facial expressions, audio features, and text to create a holistic understanding of the user’s emotional state.
    • Develop fusion strategies to combine modalities effectively for improved accuracy.
  • Real-Time Emotion Recognition:
    • Implement algorithms that allow for real-time analysis of emotions as they unfold.
    • Enable the system to provide instantaneous feedback based on the recognized emotions.
  • Emotion Trend Analysis:
    • Provide insights into the temporal patterns of emotions, allowing for trend analysis over time.
    • Identify recurring emotional states and potential triggers.