scholarly journals An Innovative Approach for Face Recognition Using Raspberry Pi

2020 ◽  
pp. 103-108 ◽  
Author(s):  
Srihari. K ◽  
Ramesh. R ◽  
Udayakumar. E ◽  
Gaurav Dhiman
Keyword(s):  
Face Recognition ◽  
Security System ◽  
Innovative Approach ◽  
Raspberry Pi ◽  
Entire System ◽  
The Real ◽  
Display Devices ◽  
Printed Media ◽  
The Face ◽  
It Efficiency

The biometrics is now a days trending security method used in the industries. The face recognition is one way of applying biometrics, and liveness detection is add on security to the system which will help the security system to identify between the fake and the real identities. In this case the fake identities are photographs as printed media. And mobile or tablet as display devices. The entire system is developed on the raspberry pi board because of it efficiency with powerful architecture and theportability.

scholarly journals Face recognition system with PCA-GA algorithm for smart home door security using Rasberry Pi

2020 ◽  
Vol 8 (3) ◽  
pp. 210-216
Author(s):  
Subiyanto Subiyanto ◽  
Dina Priliyana ◽  
Moh. Eki Riyadani ◽  
Nur Iksan ◽  
Hari Wibawanto
Keyword(s):  
Face Recognition ◽  
Smart Home ◽  
Principal Component ◽  
Recognition System ◽  
Classification Performance ◽  
Security System ◽  
Raspberry Pi ◽  
Home Security ◽  
Ga Algorithm ◽  
The Face

Genetic algorithm (GA) can improve the classification of the face recognition process in the principal component analysis (PCA). However, the accuracy of this algorithm for the smart home security system has not been further analyzed. This paper presents the accuracy of face recognition using PCA-GA for the smart home security system on Raspberry Pi. PCA was used as the face recognition algorithm, while GA to improve the classification performance of face image search. The PCA-GA algorithm was implemented on the Raspberry Pi. If an authorized person accesses the door of the house, the relay circuit will unlock the door. The accuracy of the system was compared to other face recognition algorithms, namely LBPH-GA and PCA. The results show that PCA-GA face recognition has an accuracy of 90 %, while PCA and LBPH-GA have 80 % and 90 %, respectively.

Face recognition in an intelligent door lock with ResNet model based on deep learning

2021 ◽  
pp. 1-11
Author(s):  
Suphawimon Phawinee ◽  
Jing-Fang Cai ◽  
Zhe-Yu Guo ◽  
Hao-Ze Zheng ◽  
Guan-Chen Chen
Keyword(s):  
Face Recognition ◽  
Detection System ◽  
Low Cost ◽  
Ease Of Use ◽  
Raspberry Pi ◽  
Training Methods ◽  
Graphics Recognition ◽  
Face Images ◽  
The Face ◽  
Door Lock

Internet of Things is considerably increasing the levels of convenience at homes. The smart door lock is an entry product for smart homes. This work used Raspberry Pi, because of its low cost, as the main control board to apply face recognition technology to a door lock. The installation of the control sensing module with the GPIO expansion function of Raspberry Pi also improved the antitheft mechanism of the door lock. For ease of use, a mobile application (hereafter, app) was developed for users to upload their face images for processing. The app sends the images to Firebase and then the program downloads the images and captures the face as a training set. The face detection system was designed on the basis of machine learning and equipped with a Haar built-in OpenCV graphics recognition program. The system used four training methods: convolutional neural network, VGG-16, VGG-19, and ResNet50. After the training process, the program could recognize the user’s face to open the door lock. A prototype was constructed that could control the door lock and the antitheft system and stream real-time images from the camera to the app.

scholarly journals Face Recognition Door Lock System using Raspberry Pi

2021 ◽  
Vol 9 (VI) ◽  
pp. 138-145
Author(s):  
K. V. Usha Ramani
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Initial Step ◽  
Power Input ◽  
Human Interaction ◽  
Raspberry Pi ◽  
Identification System ◽  
Human Beings ◽  
The Face ◽  
Door Lock

One of the crucial difficulties we aim to find in computer vision is to recognize items automatically without human interaction in a picture. Face detection may be seen as an issue when the face of human beings is detected in a picture. The initial step towards many face-related technologies, including face recognition or verification, is generally facial detection. Face detection however may be quite beneficial. A biometric identification system besides fingerprint and iris would likely be the most effective use of face recognition. The door lock system in this project consists of Raspberry Pi, camera module, relay module, power input and output, connected to a solenoid lock. It employs the two different facial recognition algorithms to detect the faces and train the model for recognition purpose

scholarly journals Smart Home Security System using Iot, Face Recognition and Raspberry Pi

2020 ◽  
Vol 176 (13) ◽  
pp. 45-47
Author(s):  
Manoj R. ◽  
Rekha Y. ◽  
Raju R. ◽  
Sharad A.
Keyword(s):  
Face Recognition ◽  
Smart Home ◽  
Security System ◽  
Raspberry Pi ◽  
Home Security

scholarly journals Design and Implementation of Facial Recognition System for Visually Impaired using Image Processing

2019 ◽  
Vol 8 (4) ◽  
pp. 4803-4807
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Face Detection ◽  
Visually Impaired ◽  
Recognition System ◽  
Raspberry Pi ◽  
Design And Implementation ◽  
The Face ◽  
Visually Impaired Students ◽  
Facial Recognition System

One of the most difficult tasks faced by the visually impaired students is identification of people. The rise in the field of image processing and the development of algorithms such as the face detection algorithm, face recognition algorithm gives motivation to develop devices that can assist the visually impaired. In this research, we represent the design and implementation of a facial recognition system for the visually impaired by using image processing. The device developed consists of a programmed raspberry pi hardware. The data is fed into the device in the form of images. The images are preprocessed and then the input image captured is processed inside the raspberry pi module using KNN algorithm, The face is recognized and the name is fed into text to speech conversion module. The visually impaired student will easily recognize the person before him using the device. Experiment results show high face detection accuracy and promising face recognition accuracy in suitable conditions. The device is built in such a way to improve cognition, interaction and communication of visually impaired students in schools and colleges. This system eliminates the need of a bulk computer since it employs a handy device with high processing power and reduced costs.

scholarly journals Development of a secured room access system based on face recognition using Raspberry Pi and Android based smartphone

2018 ◽  
Vol 197 ◽  
pp. 11008 ◽  
Author(s):  
Asep Najmurrokhman ◽  
Kusnandar Kusnandar ◽  
Arief Budiman Krama ◽  
Esmeralda Contessa Djamal ◽  
Robbi Rahim
Keyword(s):  
Face Recognition ◽  
Raspberry Pi ◽  
Cascade Classifier ◽  
Security Issues ◽  
Access System ◽  
The Face ◽  
Access Control System ◽  
Haar Cascade ◽  
Door Lock ◽  
Access Rights

Security issues are an important part of everyday life. A vital link in security chain is the identification of users who will enter the room. This paper describes the prototype of a secured room access control system based on face recognition. The system comprises a webcam to detect faces and a solenoid door lock for accessing the room. Every user detected by the webcam will be checked for compatibility with the database in the system. If the user has access rights then the solenoid door lock will open and the user can enter the room. Otherwise, the data will be sent to the master user via Android-based smartphone that installed certain applications. If the user is recognized by the master user, then the solenoid door lock will be opened through the signal sent from the smartphone. However, if the user is not recognized, then the buzzer will alert. The main control circuit on this system is Raspberry pi. The software used is OpenCV Library which is useful to display and process the image produced by webcam. In this paper, we employ Haar Cascade Classifier in an image processing of user face to render the face detection with high accuracy.

scholarly journals Smart Authentication and Secured Engine Unlocking System for Automobiles

2021 ◽  
pp. 653-659
Author(s):  
Bhargava R ◽  
Amulya H C ◽  
Jyothi K P ◽  
Keerthana R
Keyword(s):  
Real Time ◽  
Human Life ◽  
Security System ◽  
Raspberry Pi ◽  
Biometric Authentication ◽  
Servo Motor ◽  
Smart System ◽  
The Real ◽  
Alert Message ◽  
Vehicle Security

As human life started to evolve on this earth, the craving for smart automobiles has increased, and adding a vehicle security system to secure the automobile from theft in parking and in unsecured places is important. This paper proposes the design and development of smart system to prevent theft that uses biometric authentication to access the door and to start the engine of the automobile. This system initially uses the fingerprint module that takes the real time fingerprint of a person trying to open the vehicle door and compares it with the authorized person’s fingerprint and then allows or denies the access to door, and secondly the camera takes the image of a person trying to start the engine and compares with the authorized person’s image to allow or deny the access to the engine. In case of detection of unauthorized fingerprint, the GSM module sends the message to the owner and in case unauthorized person detected by camera it sends the captured image with alert message to owner. The system is developed using raspberry pi, GSM module, fingerprint module, pi camera, dc and servo motor.

scholarly journals Smart Security System Using IoT, Face Recognition and Processor to Processor Communication

2021 ◽  
Author(s):  
Indhuja G ◽  
Aashika V ◽  
Anusha K ◽  
Dhivya S ◽  
Meha Soman S
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Security System ◽  
Raspberry Pi ◽  
Security Systems ◽  
Serial Interface ◽  
Recognition Algorithms ◽  
Alert Message ◽  
Face Detection And Recognition ◽  
Present World

In the present world the security of the home, banks, shops, etc., are the prime concerns. The traditional security such as Closed-Circuit Television (CCTV) cameras are very easy to break and lead to theft. And moreover, the installation cost of the security systems is costlier. To overcome these problems, we are presenting Internet of Things (IoT) based solution where we can setup a smart security system. In this paper, we are proposing the system with the help of face detection and face recognition algorithms to secure our home which gives us the facility of entire surveillance of our buildings remotely and take appropriate action if anything goes wrong. The Camera Serial Interface (CSI) is attached to the Raspberry PI which detects presence of person using Face detection and recognition algorithms. The multiple Raspberry PIs attached in different areas of our buildings are connected to the main Raspberry PI which acts as hub module. If the person is identified as unknown, the information is sent to Hub module which in turn sends the alert message and live video streaming to the user using an app which we developed.

scholarly journals GSM based Face Recognition using Pir Sensor on Raspberry Pi 3

2019 ◽  
Vol 8 (12) ◽  
pp. 329-332
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Raspberry Pi ◽  
Face Area ◽  
Detection And Identification ◽  
Face Recognition System ◽  
Characteristic Views ◽  
The Face ◽  
Area Unit ◽  
Human Faces

We Developed An Associate Approach To The Detection And Identification Of Human Faces And Describe A Operating, Near-Real-Time Face Recognition System That Tracks A Subject’s Face And So Acknowledges The Person By Comparison Characteristics Of The Face To Database. Our Approach Treats Face Recognition As A Two-Dimensional Recognition Downside, Taking Advantage Of The Very Fact That Faces Area Unit Area Unit Normally Upright And Therefore Is Also Delineate By A Small Set Of 2-D Characteristic Views. Face Pictures Are Projected Onto A Feature Area (“Face Space”) That Best Encodes The Variation Among Database Images. The Face Area Is Outlined By The “Eigenfaces”, That Area Unit The Eigenvectors Of The Set Of Faces; They Do Not Essentially Correspond To Isolated Options Like Eyes, Ears, And Noses. The Framework Provides The Flexibility To Be Told To Acknowledge New Faces

scholarly journals PENERAPAN EIGENFACE UNTUK COMPUTER BASED TEST (CBT) PENERIMAAN MAHASISWA BARU SEKOLAH TINGGI TEKNOLOGI ADISUTJIPTO

Compiler ◽  
2017 ◽  
Vol 6 (2) ◽  
Author(s):  
Haruno Sajati ◽  
Astika Ayuningtyas ◽  
Dwi Kholistyanto
Keyword(s):  
Face Recognition ◽  
Computer Technology ◽  
Digital Image ◽  
Facial Recognition ◽  
Security System ◽  
The Internet ◽  
Accuracy Rate ◽  
The Face ◽  
Computer Based ◽  
Or Applications

One of the development of computer technology is the availability of systems or applications that help human work everyday so that can be resolved quickly and correctly. The system, one of which is Computer Based Test (CBT). CBT is an application used for tests conducted using computers that are in the application there are some features of CBT security when working on the problem. CBT can use a stand-alone computer, a computer connected to a network or a computer connected to the internet. Facial recognition is a type of biometric application that can identify specific individuals in a digital image by analyzing and developing face patterns. In its implementation, CBT has a weakness in the security system that becomes the gap of CBT users to commit fraud, therefore required a good security system with the creation of CBT applications that use eigenface algorithm. It is necessary to have a security system that overcomes the problem that is required identification of face recognition of participants during the test so that cheating can be reduced. The results of the test using eigenface algorithm accuracy rate reached 82%, some things that affect the level of accuracy is, the intensity of light, facial position and the use of accessories on the face.

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