This project aims to implement a robust face recognition system using MTCNN (Multi-Task Cascaded Convolutional Neural Networks) and Face-Net from Pytorch. The core functionality includes face detection, feature extraction, and identification of individuals based on their facial features.
Face recognition technology is increasingly being used in various applications such as security, authentication, and social media. This project leverages state-of-the-art machine learning models to provide accurate and efficient face recognition capabilities.
Face Detection and Face Recognition are distinct yet complementary processes in the realm of computer vision and biometrics:
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Face Detection:
- Objective: To identify and locate faces within an image or video.
- Functionality: It involves detecting the presence of faces and drawing bounding boxes around them. It does not involve identifying or recognizing who the faces belong to.
- Use Cases: Surveillance systems, camera focus, facial landmark detection, and as a preliminary step for face recognition.
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Face Recognition:
- Objective: To identify or verify a person by comparing and analyzing patterns based on the person's facial features.
- Functionality: It uses the results from the face detection phase and further processes the detected faces to determine the identity of the individuals. This typically involves feature extraction and matching against a database of known faces.
- Use Cases: Access control, user authentication, tagging people in photos on social media, and personalized user experiences.
This project focuses primarily on the face recognition aspect, aiming to accurately identify individuals based on their facial features.
- Depth analysis
- Perspective analysis
- Facial Expression analysis