Face-Recognition
Project Description:
A Facial recognition system is a well-known technology capable of identifying or verifying a human face in a digital image, video or a live video source from a camera. This technology detects people’s faces and recognises who the person is. It works by reading the facial features of a given image and compares them with facial features stored within a database. There are two major parts to this project as listed below.
- Face Detection
- Facial Reignition
This project is suitable for security purposes for recognizing humans and can be used for surveillance, medical centre, retail stores, etc. For example, this recognizer can work like a home assistant unlocking a door automatically for home users when users are present at the door. If the facial feature of a face matches with a face feature within a database, it can provide user access into the house with some interface that can be implemented. However, Face Recognition is the core of identification, so this project mainly focuses on the core alone.
Project Aim:
This aim of this project is to setup face detection and facial recognition features of the overall face reignition project. The initial aim of the project was to design and program a facial recognition system that can identify human’s faces in real-time and making sure that is suitable for security purposes, whether somebody can rely on this system or not.
The project is divided into several different objectives parts that are required to complete this project.
- To read a live video camera for detection & recognition
- To detect a face from a video camera or given image
- Gather and train the data for face identification
- To be able to Recognize a face
- Use Anaconda distribution to install OpenCV to identify faces within the image
- Use Haar Cascade to solve the problem of detecting a face
- Gather all the face data and stored it in the database and assigned ID’s to the gathered face data
- Train a classifier to test an image along with a camera for live testing.
The chosen solution requires the use of Anaconda, Python, OpenCV and HaarCascade to be able to process the image or video for recognizing a face. Also, a camera is required to discover & identify the face features over live video. Importantly OpenCV was a huge step to this project since it uses machine learning algorithms to search and identify faces within a picture.
Block diagram of Face Recognition:
Face Recognized:
