Sun. Jun 26th, 2022
Raspberry PI

The face recognition-based unlocking technology introduced on the part of Apple in the iPhones in 2017 has taken all the globe by surprise and has proven to be an innovation since then in the field of smartphone technology. Similar to home security, control and monitoring systems are now an integral part of individuals’ lives. The earlier blog explained the whole range of sensors that are present in commercial products as well as the common ideas of the maker community.

This project is an unlocking system for doors that uses a facial recognition system to create passwords. It makes use of the Raspberry Pi 4 board along with the Raspberry Pi V2 camera module as well as the 7-inch touchscreen display to display the key faces and also configure the faces that are used as the primary ones. Learn to create a face recognition door lock, using raspberry PI from top instructors


Raspberry Pi 4B

Raspberry Pi Camera Module V2

Jumper Cables

Servo Motor

The LED Touch Screen is 4.3 inches

Raspberry Pi4 USB-C Power Supply

Introducing the Project

Computing with vision is a subject of intense research for a long time because the applications are endless. Common uses of image processing and computer vision comprise depth perception, text recognition visual odometry (VO) detection of objects and their recognition as well as face estimation, pose estimation, human recognition, and detection, among numerous other things. Best ways to learn face recognition door lock system using raspberry pi, from experts

Recognizing objects and objects in the camera’s field of vision is an essential task that is utilized in robotics, automation as well as manufacturing.

The Difference Between Detecting and Recognizing a Face

The process of Face Detection is the process of applying methods for processing images to determine if an image depicts a face, or not. It’s similar to the detection of objects, but face detection has distinct, consistent characteristics of the human face making face detection a more accurate task that can be accomplished by hand-tuning feature descriptors, as well as traditional imaging techniques. The process of face detection serves as the prelude to face recognition. Recognition of the face in a scene that includes many people or objects creates problems in the localization of boundaries of the face.

The idea of this is to look for common characteristics of the face of a human in the picture. Common human facial features include:

Two eyes (blackish Blobs) separated vertically

A nose between the eyes positioned vertically below

A flat rectangular/elliptical surface above the eyes

The lips that are below the nose, stretching horizontally

Although there are many similar features, the common face detection algorithm is a fairly complex algorithm.

The process of Face Recognition can be a much more complicated task that has to determine whether the face of the person being recognized matches the faces the software has previously seen. It is essentially an object-classification-cum-object-detection task for human faces. The added complexities arise when the goal is to build a face recognition algorithm more robust to perform effectively on images that have different orientations, partial views, varying expressions and facial hair variations.

Common Techniques for Face Detection & Recognition

The Viola-Jones algorithm used for face detection with Haar The feature-based cascade classifier is a well-known method of image processing. Histogram of Gradient (HOG)-based feature descriptors are commonly employed for the Haar cascade technique. OpenCV is the biggest open-source computer vision software and includes an inbuilt version of Haar’s Cascade facial detection algorithm, which includes a trained classifier that is pre-trained to recognize human faces.

Neural networks are the most efficient concept in machine learning. It can be trained to complete tasks using training examples. Convolutional neural networks (CNNs) are which are a method of supervised learning that utilizes the concept of the convolution of a feature detector over an image input to identify the common feature descriptors within the image. CNN’s are CNN is a kind of neural system called a deep network (a neural network that has a lot of layers of multi-perception) with the capability to discover various characteristics from a variety of image training models (labeled using class names) and, consequently can identify any new image that belongs to one of the image classes that have been trained.

Project Outline

The application employs deep learning methods for facial recognition and, if the spotted face is in line with the faces within this application, then it will send an alert to the door that it needs to open. The application makes use of the Raspberry Pi camera module V2 to continuously watch live video feeds and recognize human faces within it. If the face of the person matches the key human face is configured and it is able to unlock the door.

The program trains itself with these images that are stored inside the Project folder and annotated with the name of the individual. If the picture in the live feed is of an image that is similar to the one trained images the password will be accepted.

The entire application is created using Python. Python programming language. Utilizing off-the-shelf library libraries for face recognition, as well as the Python library for servo motor control the whole project is built.

User Interface

The user interface of the application was created with the PyQt5 library written in Python. The GUI displays a simple screen that shows the live feed from the camera is shown as well as the status of doors that are locked or unlocked is additionally mentioned.

The users are strongly encouraged to consider changing the GUI to include more options to automate and add new password faces as well as different views within the user interface.

Setting up the Hardware

Hardware interfaces consist of these steps

1. Attach the Raspberry Pi Camera Module V2 to its Raspberry Pi board by using the ribbon cable with 40 pins

2. The Touch Screen Display Board to your Raspberry Pi board using another 40-pin ribbon cable

3. Connect the servo motor to the Raspberry Pi board by using the GPIO pins

A. Connect the motor’s PWR and GND pins with the GND and Vcc pins on the RPi.

b.Connect this signal pin with pin 17 on the RPi. Because the RPi utilizes software PWM, various pins are able to generate PWM signals to the motor.

4. The Raspberry Pi is a powered-up Raspberry Pi board using an AC-DC power converter that provides 5 V of power


Raspberry Pi is the favorite maker platform for single-board computers utilized by makers and hobbyists. Although this program runs on an internal battery, using a 5V/1 A AC-DC source of power is the most durable and cost-effective option.

Recognition and detection of human faces have also been a crucial aspect of security and tracking. While the algorithms and tools that are used in this library aren’t as secure and robust as commercial systems, however, they have plenty of potential as an alternative with just some designs and adjustments (beyond the limitations that this post). Users are encouraged to experiment with improvements to the library and tell us what they discover!

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