automated facial recognition door

Project Title

automated facial recognition door

Project Area of Specialization Computer ScienceProject Summary

The proposed face recognition door lock security system has been developed to prevent robbery in highly secure areas like home environment with lesser power consumption and more reliable standalone security device for both Intruder detection and for door security. This system is powered by raspberry pi circuit.Privacy and Security are two universal rights and, to ensure that in our daily life we are secure, a lot of research is going on in the field of home security, and IoT is the turning point for the industry, where we connect everyday objects to share data for our betterment. House security matters and people always try to make life easier at the same time. That’s why we put up with this project, Face Recognition Door Lock System. Facial recognition is a well-established process in which the face is detected and identified out of the image. We aim to create a smart door, which secures the gateway on the basis of who we are. We want to develop this system based on Raspberry-pi 3, to make the house only accessible when your face is recognized by the recognition algorithms from Open CV library and meanwhile you are allowed in by the house owner, who could monitor entrance remotely. By doing so, the system is less likely to be deceived: since the owner can check each visitor in the remote console, getting recognized by the camera using a photo won’t work. I want to add passcode function for entrance in case that face recognition part corrupts.

The major drawbacks in a common door lock are that anyone can open a conventional door lock by duplicating or stealing the key and its simply impossible if we want our friends and family to enter our house, without being actually present over there. Thus why not just eliminate these problems. So, to simply convert this normal door lock into a smart lock, which can open the door whenever we turn up in front of the gate or when we want it to open up for someone else without being physically present, we need to modify the door. So an era has come where devices can interact with its users and at the same time ensure of their safety and keep improvising themselves.

Project Objectives

A day to day home security level grown up to provide security to our house IOT based face recognition can be implemented. A standard web camera to capture the image to identify the visitor. It’s a method that identifies the visitor. If the face recognizes visitor, it will greet them by name and the door will be unlocked name opened. If they are not identified door will unlocked. The system will perform detection and recognition rapidly in real time when face in front of camera. This project basic utilizes the camera, and then internet connection to create a door unlocks itself by facial recognition. If the user at the door is recognized, door will be unlocked! This project is mainly for future features: safety, monitoring, security and control to home automation. Firstly the system needs a face authentication for the visitor to be able to enter the home (lock/unlocked). When an unauthenticated tries to log into system, this face will be capture the image of visitor And it will be sent to Gmail address to an admin person. The system should also support the password unlocked system.The Face is commonly used biometric to recognize people. Face recognition has received substantial attention from security guard due to human activities found in various applications of security like forensic, airport, face tracking, criminal detection, etc. Compared to other biometric traits like palm print, finger print, palm print etc. They can be taken even without visitor knowledge and further can be used for security based applications like criminal detection, face tracking, airport security, and forensic etc. Face recognition involves capturing face image from a from a web camera. They are capture image of visitor and compared image with the stored database. Classify them with known classes and then they are stored in the database. Face biometrics is a challenging field for researchers with various limitations imposed for machine face recognition like variations in change in illumination, head poses, facial expression, occlusion, aging etc. Various approaches were suggested by researchers in overcoming the stated. Automatic face recognition involves feature extraction and face recognition, face detection. Face recognition algorithms are classified into two classes as geometric feature based and image template based. The template based methods compute correlation between one or more model templates and face to find the face identity. Principal component analysis, kernel methods, linear discriminate analysis etc. are used to create face templates. The geometric feature based methods are used to analyze explicit local features and their geometric relations .Multi resolution tools such as ridge lets were found to be useful for analyzing information content of images and found its application in pattern recognition, and computer vision, image processing.

Project Implementation Method

This project work proposes an idea of for face reorganization concept for accessing the door lock system and it implemented with the help of OpenCV, which is a popular computer vision library. Face recognition is an important application of image processing owing to its use in many fields. An effective face recognition system based on OpenCV is developed in the project. Face recognition has been the best choice for the problem of biometrics and it has a various type of applications in our present life. An efficient face recognition system can be of great help in forensic sciences, identification for law enforcement, authentication for banking and security system, and giving preferential access to authorized users i.e. access control for secured areas etc. A real-time door lock access system by face recognition system. The algorithm used here is Local Binary Patterns Histograms (LBPH), based on Haar Feature-based Cascade Classifiers is presented in the project. The technique used here we will work with face detection. Initially, the algorithm needs a lot of positive images (images of faces) and negative images (images without faces) to train the classifier. Then we need to extract features from it. For this, Haar features shown in the below Fig-3.1 are used. They are just like our convolution kernel. Each feature is a single value obtained by subtracting the sum of pixels under the white rectangle from the sum of pixels under the black rectangle..

Authentication is one of the significant issues in the era of the information system. Among other things, human face recognition (HFR) is one of the known techniques which can be used for user authentication. As an important branch of biometric verification, HFR has been widely used in many applications, such as video monitoring/surveillance system, human-computer interaction. This project proposes a method for automatic door access system using face recognition technique by using python programming and from OpenCV library Haar cascade method. Object Detection using Haar feature-based cascade classifiers is an effective object detection method proposed by Paul Viola and Michael Jones. This is the standalone security device has been developed by using Raspberry Pi electronic development board and operated on Battery power supply, wireless internet connectivity by using USB modem. Automatic e-mail notification has been achieved by sending security alert mail to the user e-mail id. This proposed is more effective, reliable, and this system consumes very less data and power compared to the other existing systems

Benefits of the Project

If you’re planning to make anything “smart” about your house, then security should be where you start. In any case, all your most precious valuables live here, and protecting them should be a top priority. A smart home security system helps you to keep tabs on your house while you’re away, from the convenience of your laptop or smartphone.

Traditionally, a security provider would send a technician to install a wired system in your home and enroll you in a professional monitoring service. The story has changed. With the advent of smart technology, you can even set up the smart system on your own. On top of that, you serve as the professional monitor, getting real-time updates and notifications on your smart device.

Facial recognition means that the smart camera you use in the home can associate a face with an identity. Therefore, for facial recognition to work, you’ll need to tell the system which face belongs to which name. That means that to apply facial recognition in home security systems, you’ll need to create profiles for friends, relatives, and others you want the system to identify. You’ll then be able to know when they are at your door or going into the house.

Interestingly, with facial recognition, you can also set select alarm criteria. For example, you can set alerts that notify you when somebody whose face the camera doesn’t recognize attempts to enter your home.

Smart technology has continued to grow in leaps and bounds. Companies are now starting to offer smart door locks with facial recognition. With a smart door lock with facial recognition, you can unlock your doors with nothing but a smile. You can, however, lock and unlock that smart door using other ways, including a pin code or sometimes even a physical key. You can also set up your smart home lock in such a way that if a blacklisted person attempts to open your smart door lock, the system will send you an emergency alert.

Technical Details of Final Deliverable

• You'll need to have Windows 10 (version 10.0.10240 or better) running on your development machine.

• You'll need the Windows 10 IoT Core Dashboard on your development machine.

• You'll need to image an SD card with Windows 10 IoT Core - minimum supported OS version for this project is build 10.0.14295.

• You'll need Visual Studio 2015 Update 2.

Installing all the prerequisites could take up to an hour or so but most of that time is unattended while the bits download and get installed so plan accordingly. Microsoft has documented the setup process well in a step-by-step guide.

With respect to Visual Studio 2015 Update 2, you may need to customize your installation (if you are installing it for the first time) or modify your existing installation to ensure that you have the most recent version of the Window 10 SDK installed - the Windows 10 SDK is not installed by default in the the Community Edition and it is required for this project (I believe it is installed by default in the Professional and Enterprise editions).

The following components are required for this project. In the United States, total component costs are less than $75.

• Raspberry Pi 3 (this project works equally well on the Raspberry Pi 2 but you would then need a separate WiFi dongle if you want to go wireless) (you could also use an Intel MinnowBoard Max or a Qualcomm DragonBoard 410c).

• Passive Infrared Motion Sensor

• Supported webcam (I used the Microsoft LifeCam HD-3000 but others are support as well; check the list here)

• Speaker (plug the speaker's 3.5mm audio jack directly into theRaspberry Pi; I'd recommend powering the speak separately)

• Jumper wires
• The security system is comprised of a PIR sensor, speaker, and webcam connected to a Raspberry Pi 3. The PIR sensor will detect when someone enters your room. The speaker plays the text to speech messages. And, the webcam will capture an image which will be processed by the facial recognition service.

• First, connect the PIR motion sensor's pins to the Raspberry Pi using jumper wires - 5V on the sensor to 5V PWR on the Pi (I used pin 2), GND on the sensor to GND on the Pi (I used pin 6), and signal on the sensor to a GPIO pin on the Pi (I used pin 29 / GPIO 5). Double check the data sheet for your sensor to make sure you correctly identify the sensor's 5V, GND, and signal pin as their order varies by manufacturer. (I bent the pins on a female header so that I could mount the sensor upright on the mini breadboard.)

  1 / 2 • Plug in your webcam and speakers and wire up the PIR sensor.

Final Deliverable of the Project Hardware SystemCore Industry ITOther Industries Others Core Technology Internet of Things (IoT)Other Technologies Artificial Intelligence(AI)Sustainable Development Goals Reduced InequalityRequired Resources