Developing a face recognition system can be a challenging project. You must consider the amount of processing resources needed to process a live video feed. You will also need to interface with the cameras and receive the feed. Your final solution will involve several cameras. For example, you may need to use two cameras for the same task. This will require extensive data collection and processing. If you’re trying to make a facial recognition system for a retail store, you should consider adding a second camera to the system.
To create a facial recognition system, you must first understand the process of how the process works. In a nutshell, the project involves developing an algorithm that can accurately identify a person based on the position and size of their face. It then compares that template to a stored template and determines the tolerance of the face image. Once this threshold is determined, the controller will open the door system. The process of face recognition involves a technique called Eigen faces. This method projects an image onto a face space, defines variations of test images, and compares new image to known ones.
A successful face recognition system project should include a face loop that compares the selected faces against each other. Face loops can also include classifiers for the eyes and smile detection. Using multiple classifiers will cause the system to process data much more slowly. A desktop computer with enough power to run the HaarCascades method will work better. If you’re working on a personal project, consider making a public release of the final system to share with the public.
