Real Time emotion analysis (Sound and face) using python, deep neural networks

Introduction

This tutorial is designed to guide you through the process of real-time emotion analysis using both sound and facial recognition with Python and deep neural networks. By the end of this guide, you will be able to build a system capable of detecting emotions in real-time, which can be useful in various applications such as mental health monitoring, customer service enhancements, and interactive gaming.

Step 1: Setting Up Your Environment

Before you start coding, ensure you have the necessary tools and libraries installed.

• Python Installation: Make sure you have Python installed on your machine. The recommended version is Python 3.7 or higher.
• Install Required Libraries: Use pip to install the required libraries:

  pip install tensorflow opencv-python librosa numpy matplotlib
  

• Clone the Repository: Download the code from the GitHub repository.

  git clone https://github.com/Vineeta12345/Real-time-emotion-detection
  

• Download the Dataset: Access the dataset from the provided Google Drive link and place it in your project directory.

Step 2: Understanding the Code Structure

Familiarize yourself with the structure of the code in the repository. Here are the key components:

• Emotion Detection Model: Contains the model architecture using deep learning for both sound and facial emotion analysis.
• Preprocessing Scripts: Scripts to preprocess audio and video data for input into the model.
• Real-Time Detection Script: The main script that runs the emotion detection process in real-time.

Step 3: Preprocessing the Data

Before feeding data into the model, it must be preprocessed.

• For Audio Data:

  • Use the librosa library to extract features like Mel-frequency cepstral coefficients (MFCCs).
  • Example code snippet for audio preprocessing:

    import librosa
    audio, sr = librosa.load('audio_file.wav')
    mfccs = librosa.feature.mfcc(y=audio, sr=sr, n_mfcc=40)
    

• For Video Data:

  • Capture video frames using OpenCV and detect faces using a pre-trained model.
  • Example code snippet for face detection:

    import cv2
    face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
    video_capture = cv2.VideoCapture(0)
    

Step 4: Training the Emotion Detection Model

Once your data is preprocessed, you need to train your model.

• Load Preprocessed Data: Ensure your audio and video data is ready for training.
• Define Model Architecture: Create a neural network model using TensorFlow or Keras.
• Train the Model: Use the following code structure:

  model.fit(x_train, y_train, epochs=50, batch_size=32, validation_split=0.2)
  

Step 5: Implementing Real-Time Emotion Detection

With your model trained, you can now implement the real-time detection feature.

• Capture Video and Audio: Use OpenCV to capture video frames and a library like sounddevice for audio.
• Process Each Frame: For each frame, detect the face and extract audio features, then pass these into your trained model.
• Make Predictions: Use the model to predict emotions:

  predictions = model.predict([audio_features, video_features])
  

Conclusion

In this tutorial, you learned how to set up an environment for real-time emotion analysis using sound and facial recognition. You covered the necessary steps from setting up your environment, preprocessing data, training a model, and implementing real-time detection.

For next steps, consider enhancing the model's accuracy by using more sophisticated architectures, or try integrating the system into a user-friendly application. You can also explore other datasets to improve your model's robustness.