Face detection is a method for locating or identifying human faces in digital pictures. Face recognition is a technique for recognizing or validating an individual's identification by looking at their face. Face detection is not the same as facial recognition. Face detection is concerned with detecting the existence of faces in a picture, whereas facial recognition is concerned with determining which face it is.
Face detection is a form of computer vision that aids in the recognition and visualization of human faces in digital pictures. This approach is a type of object identification technology that detects instances of semantic objects of a given class (such as people, buildings, and automobiles) in digital pictures and videos. Face detection has become increasingly important as technology has advanced, particularly in sectors such as photography, security, and marketing. Build a face recognition system with python and OpenCV Haar Cascade face detector and LBPH face recognizer. If you wish to try this facial recognition system with your camera, make a folder with images of your face.
The repository for the tutorial of the same name can be found here. The goal is to expose people to the idea of object detection in Python using the OpenCV package, as well as how it can be used for applications such as facial detection.
• os
• opencv
• numpy
• pickle
• pillow
All major platforms, including Mac OS, Linux, and Windows, are supported by OpenCV-Python. It is possible to set it up in one of two ways:
- From pre-built binaries and source
- Unofficial pre-built OpenCV packages for Python. Packages for standard desktop environments (Windows, macOS, almost any GNU/Linux distribution) if you need only the main modules, run
pip install opencv-python We must work on three separate phases to accomplish a full project on Face Recognition:
- Face Detection and Data Gathering
- Train the Recognizer
- Face Recognition
The model can recognize a person's picture in real time. This is a non-deep learning face recognition approach that is less accurate than deep learning-based face identification but is significantly more computationally efficient and runs faster on embedded systems.
This project is built by following CodingEntreprenur's tutorial in this YouTube video:
https://www.youtube.com/watch?v=PmZ29Vta7Vc&list=PLIxHGHeOyd0zy51wDjY0wjVpAjo3E93ir&index=29