Face recognition system for door unlocking

House security matters and people always try to make life easier at the same time. We developed this system based on Raspberry-pi 3, to make the house only accessible when your face is recognized by the recognition algorithms from OpenCV library and meanwhile you are allowed in by the house owner, who could monitor entrance remotely. We also added passcode function for entrance in case that face recognition part corrupts.

Objectives Users could operate on a touchscreen to select entering the house by recognizing face or entering passcode. For face recognition, an image will be captured by pi camera and preprocessed by Raspberry pi like converting, resizing and cropping.

Then face detection and recognition are performed. One of the Raspberry pi, called Pi A, provides most services of our door lock system. It is connected with the pi camera, keypad, servo and a touchscreen. All these functions are triggered by pressing corresponding touch buttons on the PiTFT screen. The other Raspberry pi, Pi B, works as a server which receives images from Pi A and then sends response to Pi A after users operate on its touchscreen.

Attach matrix 7-pin interfaces to 7 free GPIO pins. The servo is powered by a 6V-battery pack. Rasp Pi A. Otherwise the recognized image will be displayed until remote confirmation is made. Each pressed number on the keypad will be displayed on screen and then be covered by a star if the next key has been pressed. The new passcode need to be entered through the keyboard too and will take effect next time.

For security reason, we set the upper limit for times of entering wrong passcode. Please try again after 5 minutes. Pi B works as server waiting for connection from Pi A and its user interfaces only show up when Pi B receives an image. Since there are many levels of touch buttons and we need to display various messages in different circumstances, we observed pygame window on computer monitor and debugged our user interfaces layer by layer.

We tested the sensitivity and reaction time of each touch button and experimented with different sleep time to make sure buttons all work well. Besides these, we also did some corner tests to see what would happen. After preprocessing, like resizing and cropping, the image will be used as input of Haar Cascade Classifier to detect whether there is a single face detected in this image.

Face detection is a process of finding out the face area in the image. In the project, we use Haar Cascade to detect faces. Haar-like features such as edge features, line features and center-surround features are used and they are inputs of classifiers. Cascade classifiers test the image by cascade features.

Since the amount of features is large, instead of applying all features on the window, the features are divided into different stages. The window will be tested stage by stage and initial stages usually have less haar-like features. The window which successfully passes all the stages is considered to be face image. Haar cascade classifiers han an advantage of its fast detection speed compared to other classifiers.

We tried to resize captured images to different sizes and different classifier tuning parameters like scale factor, minimum number of neighbors, to find out the best configuration for face detection.

If a single face is detected by Haar Cascade classifier, the face will be cropped out of the scene. Then the Eigenface classifier that has already been trained by prestored image library, will try to recognize the cropped face and return the confidence of its prediction at the same time.Face recognition technology entered the car market in Since then the major application has been improving safety by monitoring driver fatigue and inattention. However, a facial recognition system has the capacity of bringing many more useful features for increased convenience.

The state-of-the-art biometric technologies are proving indispensable in reducing this number. A typical system monitors eye blinking, facial expressions and head movements. If the driver ignores earlier warnings, the system might initiate automatic emergency stopping of the car. Car owners can also set up different parameters such as limits on audio volume or vehicle speed, or initiate the requirement to attach the seat belt for other people who are driving the car.

Basically, this feature allows parents to keep control of their children learning to drive. With a facial recognition system installed, you can both unlock and start the car. Mounted cameras under the windows of the doors in a Jaguar Land Rover are able to capture still images and videos of the people walking and standing by the car.

Taken one level higher, the system could also be used to modify more advanced performance settings such as throttle response, gearshift patterns and suspension stiffness. This site uses Akismet to reduce spam.

Learn how your comment data is processed. By Pavelas. In TechnologiesUse cases. Tags: Face recognition in carsFace recognition systemFace recognition technology.

Fingerprint and facial recognition on mobile phones. Leave a Comment. About Us. NET Resources.

Open a Door using Face Recognition

Contact Us.Add the following snippet to your HTML:. Read up about this project on. Bring the power of face unlock to your shelf, door or wardrobe with Bolt IoT. Welcome, curious pal! We live in an internet revolutionized era where it is now easier than ever to experiment and innovate ourselves to come up with brilliant ideas that can have a positive impact on millions around the world.

Ever wanted to add a little bit of extra security to your shelf, drawers, wardrobes or doors at home? When it comes to innovation using internet, among thousands of platforms and tools available to us, a couple that stand out are Arduino and Bolt IoT.

In this project, we'll modify a standard shelf to have a security system that unlocks using Face Verification.

face recognition system for door unlocking

We'll build a Windows Forms Application in C that can store, verify and unlock trusted faces. A synopsis of Capstone Project done as a part of this training can be found here. A lot of these concepts came in handy during the course of development of this project. So a big shout out to the Internshala Team for making this possible. We'll be using C to code. This and this are good resources to get started.

In this tutorial, I'll only be explaining code using snippets from the project that does main and important functions. It'll be tedious and unnecessary to go through the entire code as most of it is self explanatory and well documented. Our Visual Studio project makes use of 3 libraries for various purposes.

They are:. NOTE: For clarity in usages of different methods in the above APIs, please refer to their respective documentations herehere and here. If you haven't already, to to cloud. Follow the instructions given in the app to link your device with your account.

This involves pairing the Bolt with local WiFi network. Once successfully linked, your dashboard will display your device. It's a free platform that offers various kinds of image recognition services. We use it for facial identification. Create an account and go to the FacePlusPlus console. We create a new global instance of the Bolt class called myBoltthrough which we'll do all the future communications with the WiFi Module:.

Windows IoT: Facial Recognition Door

That said, now let's see how our application performs some of the core functions. This'll be made more clear later when we discuss the circuit schematics. This will signal the Arduino to lock the door.

This will signal the Arduino to unlock the door. We'll discuss the Arduino code and circuit design later in this tutorial. The trusted face's image data is encoded into a Base64 string and is stored locally in the machine. A list of corresponding names of each face is also stored.

face recognition system for door unlocking

In our program, to add a face, we first verify if there is a face available in the current frame. It returns a JSON response that will contain features of the detected face.

If no face is detected the response will simply be []. Once a face is detected, we save the image's base64 encoded string and corresponding name. Here's a video demo of adding a trusted face.Before Noon 9am to 1pm After Noon 2pm to 5pm. Please leave this field empty. Member Name. What is smaller, 6 or 2?

Skip to content. This paper is published in Volume-5, Issue-2, Volume-5, Issue-2, Face recognition, Local binary histograms, Keypad password, Electromagnetic lock. Face recognition based door unlocking system using Raspberry Pi.

Give proper credits, use Citation. Abstract Today we are facing security issues in every aspect. So we have to resolve these issues by using updated technology. In this project, we are using the Face recognition module to capture human images and to compare with stored database images. If it matches with the authorized user then the system will unlock the door by an electromagnetic lock.

The need for facial recognition system that is fast and accurate that continuously increasing which can detect intruders and restricts all unauthorized users from highly secured areas and aids in minimizing human error. Face recognition is one of the most Secured System than biometric pattern recognition technique which is used in a large spectrum of applications.

The time and accuracy factor is considered about the major problem which specifies the performance of automatic face recognition in real-time environments. Various solutions have been proposed using multicore systems.

By considering the present challenge, this provides the complete architectural design and proposes an analysis for a real-time face recognition system with LBPH Algorithm. In this algorithm, it converts the image from color to greyscale image and divides into pixels and it will be allocated in a matrix form and those images will be stored in the database.

If an image is detected then microcontroller will send power to the motor driver unit then the electromagnetic lock will unlock the door and it will lock again when there is no power supply to that unit. Finally, this paper concludes for the advanced implementations achieved by integrating embedded system models against the convention.

All content is copyright protected. Paper PDF. View Full Paper Last. Submit Your Paper Ask a Question. Connect with us. Ready to publish your paper? Paper Format:. Paper Format pdf Download Format. Imp Links:. Fee Structure Track Paper Status. Track Paper Status. Facebook Google Plus Twitter Blogger.Don Q. Dao Always striving to learn, Don Dao is driven by new adventures and challenges. His love for media and social interactions has led him to pursue a career in marketing. Over the years, he has developed a broad skill set in all aspects of marketing, specifically in event organization, social media marketing, and content marketing.

He enjoys working Facial recognition technology has a lot of applications that can be advantageous and disadvantageous. Face recognition technology has always been a concept that lived in fictional worlds, whether it was a tool to solve a crime or open doors. Today, our technology has developed this field significantly as we are seeing it become more common in our everyday lives. What exactly is facial recognition and how does it work? Facial recognition is a type of biometric software that is able to identify or verify a person from a digital image by mapping out their features mathematically and saving the information as a fingerprint.

Like a visual search engine tool, this technology is able to identify key factors within a very busy visual environment, making it very useful in picking out individuals even in crowded places. There are many different applications for face recognition technology, however, depending on how you use it can come with many advantages and disadvantages. Here are the pros and cons of facial recognition technology:. A facial biometric security system can drastically improve your security because every individual who enters your premise will be accounted for.

Any trespassers will be quickly captured by the recognition system and you would be alerted promptly. With a facial recognition security system, you can potentially reduce costs of hiring a security staff. The success rate is currently at a high due to the developments of 3D facial recognition technologies and infrared cameras. The combination of these technologies make it very hard to trick the system.

With such accuracy, you can have confidence that the premise is more secure and safe for you and your peers. Fully Automated Before, in order to confirm a match, security guards were needed to ensure that the system was correct. As previously stated, the technology is now so developed that this will no longer be necessary. The facial recognition technology can now fully automate the process and ensure its accuracy at a very high rate. This means that convenience and lower costs. Whether it may be a very high quality movie orfaces to store, everything requires space.

To combat this, many companies use many computers to process everything and to cut the time it takes to do so. However; until technology significantly develops, this obstacle is here to stay.

Camera Angle The camera angle has a very strong influence on whether or a not a face is processed. In order for a facial recognition system to completely identify a face, it needs multiple angles, including profile, frontal, 45 degree and more, to ensure the most accurate resulting matches.

Also, any obstructions, such as facial hair or hats, can definitely cause some trouble. All facial recognition technology emerges into the market with both promises and challenges. It is possible that in just a few years, such systems will be so advanced so as to process expressions and hand gestures within a matter of seconds. While the pros will advance, most of the cons can be reduced by human tweaking. His love for media and social interactions has led him to pursue a ca Dao - March 22, Tags : facial recognition IT-Security.

Learn about the pros and cons of facial recognition.Add the following snippet to your HTML:. Bring the power of face unlock to your shelf, door or wardrobe with Bolt IoT. Project tutorial by Divins Mathew. Welcome, curious pal! We live in an internet revolutionized era where it is now easier than ever to experiment and innovate ourselves to come up with brilliant ideas that can have a positive impact on millions around the world.

Ever wanted to add a little bit of extra security to your shelf, drawers, wardrobes or doors at home? When it comes to innovation using internet, among thousands of platforms and tools available to us, a couple that stand out are Arduino and Bolt IoT. In this project, we'll modify a standard shelf to have a security system that unlocks using Face Verification.

We'll build a Windows Forms Application in C that can store, verify and unlock trusted faces. A synopsis of Capstone Project done as a part of this training can be found here.

A lot of these concepts came in handy during the course of development of this project. So a big shout out to the Internshala Team for making this possible. We'll be using C to code. This and this are good resources to get started. In this tutorial, I'll only be explaining code using snippets from the project that does main and important functions.

It'll be tedious and unnecessary to go through the entire code as most of it is self explanatory and well documented. NOTE: For clarity in usages of different methods in the above APIs, please refer to their respective documentations herehere and here.

If you haven't already, to to cloud. Follow the instructions given in the app to link your device with your account. This involves pairing the Bolt with local WiFi network. Once successfully linked, your dashboard will display your device. It's a free platform that offers various kinds of image recognition services.

We use it for facial identification. Create an account and go to the FacePlusPlus console. We create a new global instance of the Bolt class called myBoltthrough which we'll do all the future communications with the WiFi Module:. This'll be made more clear later when we discuss the circuit schematics. This will signal the Arduino to lock the door. This will signal the Arduino to unlock the door. We'll discuss the Arduino code and circuit design later in this tutorial.

The trusted face's image data is encoded into a Base64 string and is stored locally in the machine. A list of corresponding names of each face is also stored.

In our program, to add a face, we first verify if there is a face available in the current frame.

face recognition system for door unlocking

It returns a JSON response that will contain features of the detected face. If no face is detected the response will simply be [].Add the following snippet to your HTML:. Read up about this project on. Build an automated door that unlocks itself using facial recognition. Home security systems are a growing field of projects for Makers.

A self-built system is not only less expensive than a bulky professional installation, but it also allows for total control and customization to suit your needs. This project utilizes a Raspberry Pi, basic Webcam, and an internet connection to create a door that unlocks itself via facial recognition.

If the visitor at the door is recognized, the door will unlock! How will you expand the project? What features will you add? Let us know in the comments section below! For more information on how to deploy your application on a Windows IoT device, please see this documentation. The following screenshots were taken on a PC that was set up to act as a Raspberry Pi would.

When you first run the project, this is the screen you should see. There are three buttons located on the bottom app bar.

Windows IoT: Facial Recognition Door

The first is a "plus" icon. This is used to add a new user to your "whitelist. Try pressing the "plus" button now. You should see this screen:. Position yourself or a friend in front of the webcam and press the Capture ID Photo button.

You should see this screen with your newly captured selfie:. If you're happy with the photo, enter the name of the person in the photo and press Confirm. If not, just press Cancel and take another photo. After pressing Confirm, you will be sent back to the main page, but you will now see a user under the Whitelisted Visitors section:.

This will improve the overall accuracy of the facial recognition door. You can also press the trashcan icon to delete this user.

When you're finished, press the home icon. You're now ready to unlock the door!


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