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Storing images and comparing them with the input image by using the Pi camera connected to a Raspberry Pi. If the input image is at least 80 percent of the stored image, then the solenoid lock unlocks. Or else, the door remains locked. When the input image is not equal to the stored image, that is, if a new person is near the door, the camera takes a snap of him. Also, Alexa says to the user that someone is near the door.
To verify that our daily life is going securely. A lot of research programmers are going on in this entire society. The turning point comes through the internet of things, the industry has emerged with the lots of elements provided from IoT. We can able to connect our daily life things or objects with this had successfully evolved lots of things. This Facial recognition door unlock system is a process that will detect the face and identifies the people. People are having different types of face cut, in particular, there are many unique faces that are different from each other which inspired us. From that concept, this process has been established. Our main aim is to create the smart door system to a house, that will secure the house and all your things at your home. In this concept of our system, we have been used a live web camera on the front side of the door, along with the display monitor. This web camera shows the owner/particular viewer for whom the house is his control. This shows the person who stood in front of the door. The system is set up and the voice output is being processed by the processor that is used to show the answers/instructions as the output on the screen. We are using a stepper motor that is used to lock/open then by the sliding method so that a normal person stands in front of the door and access it. This process is done through this Microsoft face API application. The display is being operated on a Microsoft Visual Studio application.
Why: Problem statement
If an unknown person locks the door, it can be known by the people inside the home. It helps the house owner to protect himself and his belongings inside the home.
How: Solution description
Face Recognition System/automatic attendance system will identify or verifies the identity of a person from digital images captured from a camera source. We utilize OPEN CV library which is a famous PC vision library that began by Intel. The cross-stage library sets its emphasis on constant picture handling and incorporates sans patent executions of the most recent PC vision calculations. The basic flow of the face recognition system/automatic attendance system is the image captured by the camera. The Viola jones method will identify the face in the picture utilizing Haar cascade classifiers and features are extricated from the face. After the extraction, the system matches the captured images with database images. The matching of the captured images and database images is done using the LBPH algorithm. The thought is to not take a gander at the entire picture as a high-dimensional vector-like in Eigen Faces and the Fisher Face recognizer algorithms. However, it is to depict just neighborhood components of a question. The LBPH algorithm is more accurate than the Eigen Faces. The complexity in the huge calculation in Eigen Faces or PCA is reduced by the LBPH algorithm. The components you extricate along these lines will have a low dimensionality verifiably. If a face is remembered, it is known, else it is obscure. The entryway will open consequently for the approved individual because of the charge of the Raspberry Pi to the entryway motor. Then again, the alarm will ring for the obscure individual.
How is it different from competition
It can be used for surveillance, that is, in CCTV cameras only. The video will be stored and we have to view all the videos. But, here snapshots are taken and are therefore easy to view. The key is not required.
Who are your customers
Home door automation.
Project Phases and Schedule
Phase 1: Face recognition system/automatic attendance system