Face Recognition Door Lock

The goals of this project were to build a modern, easy-to-use, smart door lock that allows for accessible unlocking and adds convenience, utility, and security to your home. It allows users to open their door remotely via the accompanying Smart Lock mobile app, or hands-free by using face recognition via a camera mounted on the door. The system was made up of three major components, including a cloud back-end, an on-board logical unit, and a mobile application

Development of Face Recognition on Raspberry Pi for Security Enhancement of Smart Home System

Nowadays, there is a growing interest in the smart home system using Internet of Things. One of the important aspect in the smart home system is the security capability which can simply lock and unlock the door or the gate. In this paper, we proposed a face recognition security system using Raspberry Pi which can be connected to the smart home system. Eigenface was used the feature extraction, while Principal Component Analysis (PCA) was used as the classifier. The output of face recognition algorithm is then connected to the relay circuit, in which it will lock or unlock the magnetic lock placed at the door. Results showed the effectiveness of our proposed system, in which we obtain around 90% face recognition accuracy. We also proposed a hierarchical image processing approach to reduce the training or testing time while improving the recognition accuracy

IMPROVED FACE DETECTION ACCURACY USING HAAR CASCADE CLASSIFIER METHOD AND ESP32-CAM FOR IOT-BASED HOME DOOR SECURITY

Some people are very easy to open the door lock with just a small wire. This causes the house to be vulnerable to burglary and theft. In previous studies, there were still shortcomings such as the accuracy of facial recognition was not good, the time for the facial recognition process was very long and no action was taken if the camera caught an unknown person. This raises the need for solutions related to security systems that can monitor homes when something suspicious happens so that it can be prevented immediately. This study aims to create a home door security system using ESP32-CAM as face recognition. This face recognition can unlock the door automatically and if someone is caught on camera who is not known, the system will send a notification to the owner to follow up on this. The results of the face detection test using the Haar Cascade Classifier method that can distinguish a known face and an unknown face. The results of facial accuracy at a distance of 30 cm, 40 cm, and 50 cm with a light intensity of 130 lux obtained an average accuracy of 96.6%. The result of the average time required from the face sampling process until the face is recognized by the system is 21,50 seconds

Students Attendance Management System Based On Face Recognition

Verification is a main concern with computer-based systems. Face recognition is commonly often used in many applications like protection device and door lock. Universities, Colleges, schools and library attendance are compulsory for every student. Current attendance approach is lecturer calling the roll number and name of the student and then marking attendance on a paper. It’s waste of time for everyone. Verification of every student in a large class room is quite difficult. We use automated attendance management system to prevent such failures. The paper explains how to use facial recognition to mark student’s attendance. In our system introduce the attendance management system for face recognition. Our system will store student's face images into database and then mark automatically attendance of students after this save the result in database. The attendance will be stored according to date and time. There are five main point in this system :( 1) first of all student will login with username and password. (2) After this student will be able to mark automatic attendance thought face recognition (image already stored in database). (3) Lecture will be login (4) Lecturer can allow students for attendance, view attendance (5) Admin after login can register the students, store images of students into database and train the model

Home security system using face recognition

This is a Home Security System that focuses on using face recognition. The system is divided into two main parts which are an Android application and Raspbery Pi module. Using the Home Security System application, the user will be able to sign up, sign in, reset password, upload face images (for face recognition), retrieve uploaded images, retrieving door status and motion detection history and view CCTV footage. Raspberry Pi module is equipped with a camera module (face recognition purpose), passive infrared (PIR) motion sensor, door sensor and solenoid door lock

Integrating RBF-based Neural Network Face Expression Recognition in Access System

Biometric recognition system such as facial recognition system was widely developed over the past few years. Facial recognition system is commonly used in security system to allow user to protect their privilege. The normal security like key or password is no longer relevant as people prefer an easier and flexible way. Therefore, this paper presents a better and easier way of security system that can recognize the user successfully and give the matching percentage. By using Radial Basis Function Neural Network in MATLAB, a face recognition system can be created. The RBF system will be trained by data as reference, input image will undergo the same process and the data obtained will be used to match with the data in the RBF to obtain the matching percentage. A suitable matching percentage reference was chosen from this analysis as the minimum require matching to access the security system where error rate is one of the main concerns where it is the unwanted result that might occur. Different threshold number, spread value, and sizes of dimension also tested, the differences on the output matching result were observed. By using the microcontroller to control a relay to control the magnetic door lock, the system was able to successfully control the door lock