This Face Recognition System is designed to accurately identify and classify individuals based on their facial features. It uses a combination of deep learning models and machine learning algorithms to perform face detection, feature extraction, and classification.
The system uses the MTCNN algorithm for face detection, which is trained to detect faces in images that vary in size, pose, and lighting conditions. The detected faces are then passed through the FaceNet model, which extracts high-level features from the face images and represents each face as a vector in a high-dimensional space.
Finally, the system uses a Support Vector Machine (SVM) classifier to identify and classify the faces based on their feature vectors. The SVM is trained on a dataset of labeled face images to learn a discriminative model that can separate faces belonging to different individuals.
This Face Recognition System is designed to be highly accurate and efficient, making it suitable for use in a variety of applications, such as security, surveillance, or access control. It can be easily customized and extended to meet specific requirements, such as different face datasets or classification tasks.
The system is implemented in Python and uses popular deep learning frameworks such as TensorFlow and Keras, as well as machine learning libraries such as scikit-learn. The code is open source and available on GitHub, along with detailed documentation and usage instructions.