Audio-Visual Emotion Recognition System

Project Breakdown

1. Problem Statement

The goal of this project is to develop a system that can recognize human emotions in real-time by analyzing both facial expressions (visual data) and voice tone (audio data). By fusing these two modalities, the system aims to achieve enhanced accuracy in emotion detection and classification.

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2. Key Features

Visual Emotion Recognition

• Detect faces in real-time using a connected camera.
• Classify facial expressions into categories such as happy, sad, angry, and neutral using a pre-trained or custom deep learning model.

Audio Emotion Recognition

• Capture audio input via a microphone.
• Extract features like pitch, tone, and intensity to classify emotions.

Multimodal Fusion

• Combine visual and audio data using a fusion algorithm to improve emotion recognition accuracy.

Output

• Display the detected emotion on a screen or send it to a mobile app.
• Provide real-time feedback through visual or audio cues.

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3. Technology Stack

Programming Languages

• Python (primary)
• C/C++ (for embedded optimization)

Libraries and Frameworks

• Computer Vision: OpenCV, MediaPipe, Dlib
• Audio Processing: Librosa, PyAudio, SpeechRecognition
• Machine Learning: TensorFlow, Keras, PyTorch
• Data Fusion: NumPy, Pandas, Scikit-learn

Hardware

• Raspberry Pi 4 or NVIDIA Jetson Nano (for embedded deployment)
• Camera module (e.g., Pi Camera or USB webcam)
• Microphone (USB microphone or built-in mic)
• Display (optional for real-time visualization)

Datasets

• Visual: FER-2013 (Facial Expression Recognition), CK+ (Extended Cohn-Kanade)
• Audio: RAVDESS (Ryerson Audio-Visual Database of Emotional Speech and Song), CREMA-D (Crowd-Sourced Emotional Multimodal Actors Dataset)

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4. Additional Areas of Exploration

2) Embedded Machine Learning Device Networking

• Explore the communication between multiple embedded devices (e.g., Raspberry Pi or Jetson Nano) to create a network of neural devices that collaborate for enhanced emotion recognition and processing.

3) Machine Learning on Edge Devices

• Implement machine learning models directly on edge devices to minimize dependency on a larger central system.
• Optimize models to run efficiently on low-resource hardware while maintaining real-time performance.

4) Preprocessing on Embedded Devices

• Apply machine learning on embedded devices for data preprocessing.
• Transmit preprocessed data to a central, high-capacity machine learning system for further analysis and decision-making.

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5. Potential Applications

• Suspicious Activity Detection: Identify abnormal or suspicious behavior in security-sensitive environments by monitoring emotional states.
• Mental Health Monitoring: Provide real-time emotional feedback to assist in tracking and managing mental health issues.

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This project focuses on the synergy of embedded systems, machine learning, and multimodal fusion techniques to create a highly effective emotion recognition system that can function efficiently in real-world applications.