This project implements a facial recognition system using convolutional neural networks (CNNs) and machine learning techniques. The goal of the system is to identify and recognize faces in images by labeling the corresponding people.
THEORY Face recognition is an area of computer vision that involves identifying and verifying human faces in images or videos. In this project, we used the machine learning approach, more specifically CNNs, to perform this task.
CNNs are neural networks specialized in processing image data. They are able to learn patterns and discriminative features directly from the pixels of an image, without the need for manual feature extraction.
In this project, we used the pre-trained VGG16 as the basis for the facial recognition model. This means that we loaded the pre-trained weights of VGG16, which were learned on the ImageNet dataset, and added custom layers on top to adapt the network to our specific facial recognition problem. These custom layers are trained on our face dataset to learn how to recognize and classify the people present in the images.
PRACTICE
Environment Setup Make sure you have Python 3.7 or higher installed on your system.
Install the required Python libraries by running the following command:
- pip install tensorflow keras scikit-learn opencv-python numpy matplotlib mtcnn
Project Structure main.py: the main file that coordinates the flow of the program and calls the appropriate functions to load the images, train the model, perform grid search, and evaluate the performance.
data/: a folder containing the face images used in training, organized in subfolders by person.
README.md: this current file, providing details about the project.
Usage: Make sure you have placed your face images in the data/ directory, organized in subfolders by person.
Run the main script:
- python main.py
The script will load the images, train the facial recognition model using grid search with cross-validation, and display the results.
Future Improvements This project can be expanded and improved in several ways. Some ideas for future improvements include:
Use of transfer learning: Instead of training a model from scratch, leverage pre-trained models, such as VGG16, Inception, ResNet, etc., that have been trained on large datasets, such as ImageNet. These models have already learned useful features in images and can be used as a solid foundation for facial recognition. Freeze some pre-trained layers and add custom layers on top to adapt to your dataset.
More advanced data augmentation: In addition to basic data augmentation, such as rotation and zooming, explore more advanced techniques, such as horizontal flipping, random cropping, brightness/contrast adjustment, perspective change, and others. This can help to create a wider variety of training examples and improve the model's ability to handle variations in the input data.
Implementation of multi-face detection: Modify your code to handle the detection and recognition of multiple faces in a single image. Instead of processing one image at a time, you can detect and recognize multiple faces simultaneously, which can be useful in scenarios where multiple people are present in the images.
Increasing the model's capacity: Experiment with increasing the capacity of your model by adding more convolutional layers or dense layers. This can help the model to learn more complex features and improve overall performance.
Implementation of outlier detection: Add a step to detect possible outliers or images that do not correspond to a real face. This can help to filter out images with noise, foreign objects, or corrupted data, thus improving the quality of the training set.
CONTRIBUTION: Contributions are welcome! If you have any ideas, suggestions, or corrections, feel free to contribute.