Door Lock System Using Raspberrypi

Recently, there are a large number of smart lock systems that can be used in the workplace or home, but many of them depend on finding an external key, password, fingerprint, and facial recognition, where these systems suffer from weakness as the physical key may be forgotten in a place, the password can be memorized which is hard for some peoples, and the fingerprint system sometimes does not work on people who suffer from chronic diseases, such as people with diabetes and for each of these systems the person needs a free hand so that he/her can open the lock which means that is if the person carries something he/her needs to be placed somewhere. The facial recognition system cannot recognize the person's face when the place has poor lighting. This paper proposes a home door lock system that relies on the internet of things (IoT) technology and portable electronic devices Bluetooth such as a smart mobile using the device Bluetooth media access control (MAC) address as a key and the IoT for monitoring, which leads to a door lock that does not require a free hand. The proposed lock system outcome can be a good candidate for home security applications.

This research paper talks about the development of an innovative IoT-based smart door lock that also uses facial recognition, Internet of Things (IoT) integration, and the ESP32-CAM microcontroller to increase home security. As locks continue to evolve with more new features, the use of IoT is increasing and enhancing the security and usability. ESP32-CAM was selected for this project because of its built-in Wi-Fi connectivity and camera, which render it exceptionally suited for creating a smart lock system that is able to not only recognize the saved faces but also alert the user if there is an intruder. When compared to other microcontrollers like Raspberry Pi, the ESP32- CAM offers many more advantages, including enhanced security features, a userfriendly interface, and a lower cost, and much more in initial price. This paper also compares and talks about already existing facial recognition-based door locks, providing a detailed analysis of their plus points and limitations to understand the current state of the technology. The research also provides a detailed breakdown including the system's development process, selection of components for the device, and compilation of hardware components for the implementation of the model, The paper also includes the algorithms and user interface design. In this paper, you’ll see how each component works, its usage, and how each component works together to ensure reliable and accurate facial recognition. Also, model testing was done to get the device's performance in a number of different conditions and usage areas. The results of these tests show that ESP32-CAM based smart door lock is a good and powerful solution and it is also cost-effective, offering good amount of savings compared to other smart lock systems in the market. This paper highlights how the project talks about some of the common security problems associated with the old locking system. By offering a more accessible and reliable alternative, this smart lock system has the potential to revolutionize home security. The integration of advanced facial recognition technology with IoT makes the home door lock system a more secure and user-friendly approach to home access control.

The traditional doorlock system were facing lack of security with vital issues from burglary to overcome on this the latest technologies are being adopted to clear the gap of issues. In this system the IoT-based WiFi fingerprint door lock with live streaming using a Raspberry Pi and a webcam is a smart door lock system that provides enhanced security features for homes.The system utilizes the IoT platform for to integrate & connect the lock to a WiFi network and enable remote access through a mobile device.The system also features a Raspberry Pi device that is connected to a webcam, allowing live streaming of the entrance area. Users can view the live feed of the entrance area on their mobile application or whatapp , which providing an additional layer of security. The Raspberry Pi device can also be programmed to send notifications when the door is unlocked or opened, alerting the user of any unauthorized access.Using Raspberry Pi as the main platform for the project, allowing for easy customization and development. Integrating a webcam into the system to enable live streaming. The live steaming can be viewed from web Application or from whatsapp .

In recent years, owing to the proliferation of Internet of things, homes and offices square measure being translated into good homes and offices. The paper presents an epitome of sensible door lock system which may be accustomed enhance the safety. Door locks measure a standard prevalence in our everyday lives. Yet, we tend to still trust ancient doorknobs, that use physical keys that brings with it several issues like key duplication from photos and lock-picking. The focus being security, the primary layer is the alphanumeric key security, and the next layer involves the QR code technology. QR is one of the most secure way to ensure security as it can generate a new code for each new data provided. It will be extremely helpful in tracking the number of individuals trying to access the system. The automation system includes Raspberry Pi equipped with camera and an android mobile phone. This project aims to make a door lock system as an improved alternative that accepts QR codes for the door lockup system.

Face detection entails the technology that identifies the human face in digital images. Face detection can be used in various applications. One of the major areas of application of face recognition is in security systems. We know that a smart security system is the need of the hour and has become a necessity in today’s time. It is the basic need of humans to be able to protect one’s home and belongings. The security system needs to be smart and efficient. Hence, it avoids intruders and protects your possessions by minimizing break-ins. Installing a home security system helps in protecting your home and valuables from burglars. The security system developed has a camera module that will give access to only authenticated persons to unlock the main door of the house using facial recognition technology. This discrete system is developed using an electronic board called as Raspberry Pi can detect intruders as well as send off an alarm to nearby police stations using SMS or Email. Keywords: Home security; Face Recognition; Internet of Things (IoT); RaspberryPi Microcontroller; Control; Smartphones Cite this Article Ayush Yadav, Shaam Garg, Shivam Jamloki, Aanchal Sadana, Kusum Tharani. Home Security using Facial Recognition. Current Trends in Signal Processing. 2020; 10(2): 5–11p.

Current attendance methods, though simple, are prone to manipulation and can be time consuming for both educators and students. For instance, manual systems and QR code based methods allow students to register attendance on behalf of others due to the lack of unique identification. While calling names individually improves security, it disrupts the learning process by consuming significant time. This study addresses these issues by developing an autonomous robot, CObot, equipped with a facial recognition system powered by a Raspberry Pi microcontroller. CObot navigates classrooms autonomously, avoiding obstacles, and efficiently records attendance without requiring movement from students or educators. The use of facial recognition ensures that only registered individuals can mark attendance, creating a secure and tamper-proof system. Additionally, the integration of Internet of Things (IoT) technology enables real-time data transfer to Google Sheets, simplifying record-keeping and reducing educators administrative workload. A 3D-printed, customizable car structure enhances the robot design, while the Raspberry Pi 5 was selected over alternatives like the ESP32-S3 for its superior processing power and faster data transfer speeds, ensuring smoother operations. In testing with 60 participants, the Raspberry Pi 5 demonstrated a 99% accuracy rate in facial recognition, outperforming the ESP32-S3 90% accuracy. By saving time, improving security, and reducing manual effort, CObot enhances the classroom environment, benefiting both students and educators. While the improvement in attendance systems may appear incremental, CObot represents a meaningful step toward fostering a more efficient and effective learning environment.

This project presents a Home Automation Security System that integrates face detection and recognition with RFID technology using a Raspberry Pi. The system aims to enhance security and access control in homes by automating the process of user authentication through facial recognition and RFID tags. The system utilizes a Raspberry Pi as the central control unit, a camera module for face detection and recognition, and an RFID reader to scan RFID tags for additional access control. The system functions by scanning an RFID tag, and upon detection of a valid tag, it activates the camera to capture the face of the person requesting access. The captured image is then processed using OpenCV and face_recognition libraries in Python to match the face with a pre-stored database of authorized individuals. If both the RFID tag and face match the stored data, the system grants access by performing automation actions such as unlocking doors or turning on lights. Keywords- Raspberry Pi, Pi Camera, Face Detection, Face Recognition, RFID Technology, RFID Tag Verification.

One of the ways humans communicate is by using facial expressions. Research on technology development in artificial intelligence uses deep learning methods in human and computer interactions as an effective system application process. One example, if someone does show and tries to recognize facial expressions when communicating. The prediction of the expression or emotion of some people who see it sometimes does not understand. In psychology, the detection of emotions or facial expressions requires analysis and assessment of decisions in predicting a person's emotions or group of people in communicating. This research proposes the design of a system that can predict and recognize the classification of facial emotions based on feature extraction using the Convolution Neural Network (CNN) algorithm in real-time with the OpenCV library, namely: TensorFlow and Keras. The research design implemented in the Raspberry Pi consists of three main processes, namely: face detection, facial feature extraction, and facial emotion classification. The prediction results of facial expressions in research with the Convolutional Neural Network (CNN) method using Facial Emotion Recognition (FER-2013) were 65.97% (sixty-five point ninety-seven percent).

Smart Clock Powered by Raspberry Pi

Android-based driver alert system is a real-time application which is used for detecting driver drowsiness and alerts the driver to reduce the possible accidents. In this paper, the detection is based on the results of face and eye region or the pupil of the eyeball to determine whether the driver is in the drowsy condition or not. A new approach is introduced for detecting driver drowsiness by using Google API and OpenCV library. My system uses a modified implementation of the Viola-Jones algorithm for faces and eye detection of the driver. Detection is performed on the basis of the symptoms related to eye and face region of driver. In this method, face detection module, motion detection module, on road motion detection module, GPS module are used for extracting the symptoms of driver drowsiness. Android-based smart phones are equipped with several sensors that can help to assist in safety development for drivers on the road. Comparison between OpenCV library and Google API of both face and eye detection as shown in this paper using graph. The average correction detection ratio of face detection is 100% and eye detection is 85%, which shows my proposed system is feasible, cost effective, and reliable.

This paper focuses on the initial testing, by using a DC variable load, of the laboratory scale Superconducting Magnetic Energy Storage (SMES) system developed in the University of Western Macedonia. The SMES is built using a single-pancake coil (SPC) made of 12mm YBCO tape. It is cooled to 40 K using a cryocooler. The objective is to utilize the coil in an initial experiment prior to constructing a dedicated dc to dc chopper and using the coil for power-hardware-in-the-loop experiments. SMES measurements are carried out using a Python program running on a Raspberry Pi. The DC variable load is programmed using a Raspberry Pi microcontroller and RS232 protocol.

This paper describes an expert RFID biometric enabled dual system architecture which acts as a smart and digitized banking vault door locking system. The proposed system is novel, multipurpose and provides three levels of security. It is based on locker automation using RFID technology and software Android App technology that allows access only to the authorized owner of the bank vault to open it. Despite the legacy key system, the overall control of electronic smart door lock system utilizes the RFID tags via NFC, biometric traits via web portal and mobiles via Bluetooth as the procedure for authentication. Our proposed RFID technology-based access control authentication mechanism utilizes low cost irrevertible functions to control the vulnerable security attacks. Furthermore, the proposed lock system has a tag’s identification search complexity of only $${\mathcal {O}}(1)$$ .

Recently, digital doorlock systems have been employing biometric recognition and smartphone as personal authentification means. The performance of a digital doorlock system is determined by the two conflicting indices such as security and convenience. This paper proposes and implements one grip based doorpull shaped doorlock systems using fingerprint and touch sensor grip pattern. The proposed system sequentiallyperforms fingerprint recognition and grip pattern identification when a user grips the doorpull in order to open the door. This method so called ‘One Grip’ is considered to enhance security while maintaining users’convenience. We expect the proposed method can solve the phone missing problem encountered in devel-oping smart doorlock systems based on smartphones.Key Words : Digital doorlock, One Grip, Fingerprint Recognition, Grip Pattern, Security, Convenience.Received: Jan. 5, 2016Revised : Feb. 1, 2016Accepted: Feb. 11, 2016 † Corresponding authorbhw@gwnu.ac.kr 1. 서 론

This is an IoT-enabled RFID-based door locking gadget. This door lock system will know how long the door is open and in this door lock system, only those people who register will be able to enter using their card. It uses a servo motor that operates with the help of Arduino. The Arduino board runs with complete programming that is stored inside it. By using this gadget, an owner can track his office and the place where he wants to let the specific people in. It is also very secure. RFID Module, LCD Display Red and Green and Yellow LED Light and Buzzer have also been used in this door lock system. When the door is locked, the yellow LED will be on and when the door lock is open, the green led light will turn on. The Red LED work will alert you that your card is wrong.

The purpose of this study to build a home security system based on object motion detection using Robot Operating System 2 (ROS2) and Raspberry Pi. ROS2 function in this study as a software that used to read and process the camera data then control the buzzer sound, while the Raspberry Pi hardware function to running the ROS2 on the top of Linux Ubuntu 18.04 operating system. Camera function to read the image and video data can be developing for input device to control the home security systems based on the object motion detection. The method has used to detect the object motion is Binary-Image Comparison (BIC), where this method worked by compare the value of the binary image object with the binary master to use it as a decision-making algorithm when the camera detecting the object movement based on object color. Object colors detected in this study are red, yellow, green, and blue. Each object colors are processes using OpenCV library in the ROS2 node service, after that all of the nodes will communicate through topics to communicating and exchanging the message data. This study result is the prototype has developed can give a buzzer sound warning to the user when the camera detecting the object motion based on the object's color.