Face Emotion Prediction

Overview

The Face Emotion Prediction project aims to develop a machine learning model capable of identifying and predicting human emotions based on facial expressions. The project is primarily implemented in PureBasic and Python, leveraging deep learning techniques to analyze and classify emotions from facial images.

Complexities

Data Preprocessing

• Challenge: Handling a large dataset of facial images and ensuring the quality and relevance of data.
• Solution: Implemented advanced data preprocessing techniques, including normalization, augmentation, and face alignment, to enhance the quality and diversity of the training dataset.

Model Training

• Challenge: Training a deep learning model that can accurately recognize and classify emotions.
• Solution: Utilized convolutional neural networks (CNNs) with multiple layers to capture intricate patterns in facial expressions. Employed transfer learning to leverage pre-trained models and fine-tuned them on our dataset, significantly improving accuracy and reducing training time.

Challenges and Solutions

Challenge 1: Imbalanced Dataset

• Problem: The dataset had a disproportionate number of images for certain emotions, leading to biased predictions.
• Solution: Applied techniques like oversampling, undersampling, and synthetic data generation (using SMOTE) to balance the dataset and improve model performance.

Challenge 2: Real-time Prediction

• Problem: Ensuring the model can make real-time predictions without significant latency.
• Solution: Optimized the model and inference pipeline using efficient algorithms and hardware acceleration (e.g., GPU), achieving real-time performance.

Challenge 3: Integration with PureBasic

• Problem: Integrating Python-based deep learning models with PureBasic, which is less commonly used for such applications.
• Solution: Developed a robust interface between PureBasic and Python, enabling smooth communication and execution of the trained models within the PureBasic environment.

Getting Started

Prerequisites

• Python 3.8+
• PureBasic 6.0+
• TensorFlow or PyTorch
• OpenCV

Installation

1. Clone the repository:

   git clone https://github.com/faisal-fida/face-emotion-prediction.git
   cd face-emotion-prediction

2. Install the required Python packages:

   pip install -r requirements.txt

3. Run the application:

   python main.py

Usage

1. Ensure your webcam is connected.
2. Run the application and allow it to access your webcam.
3. The application will start predicting emotions in real-time.

Contributing

We welcome contributions from the community. Please read our Contributing Guide for more details on how to contribute.

License

This project is licensed under the MIT License. See the LICENSE file for details.