Face Recognition Using Raspberry PI

2020 ◽  
pp. 1-11 ◽  
Author(s):  
Shruti Ambre ◽  
Mamata Masurekar ◽  
Shreya Gaikwad
Keyword(s):  
Face Recognition ◽  
Raspberry Pi

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 Real-Time Internet Based Attendance Using Face Recognition System

2018 ◽  
Vol 7 (3.15) ◽  
pp. 174 ◽  
Author(s):  
Yuslinda Wati Mohamad Yusof ◽  
Muhammad Asyraf Mohd Nasir ◽  
Kama Azura Othman ◽  
Saiful Izwan Suliman ◽  
Shahrani Shahbudin ◽  
...  
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Recognition System ◽  
Ease Of Use ◽  
Cloud Services ◽  
Raspberry Pi ◽  
Student Attendance ◽  
Online Student ◽  
Internet Server ◽  
Face Recognition System

This project focuses on face recognition implementation in creating fully automated attendance system with a cloud. Cloud services will provide a useful information regarding the attendance such as attendance summary performance and visualizing the data into graph and chart. In this study, we aim to create an online student attendance database, interfaced with a face recognition system based on raspberry pi 3 model B. A graphical user interface (GUI) will provide ease of use for data analysis on the attendance system. This work used open computer vision library and python for face recognition system combined with SFTP to establish connection to an internet server which runs on PHP and Node.js. The results showed that by interfacing a face recognition system with a server, a real-time attendance system can be built and be monitored remotely.  

scholarly journals Real time Implementation of Face Recognition System on Raspberry Pi

2018 ◽  
Vol 7 (2.17) ◽  
pp. 85
Author(s):  
K Raju ◽  
Dr Y.Srinivasa Rao
Keyword(s):  
Face Recognition ◽  
High Performance ◽  
Recognition Rate ◽  
Cost Effective ◽  
Recognition System ◽  
Raspberry Pi ◽  
Face Recognition System ◽  
Effective System ◽  
Face Detection And Recognition ◽  
Improved Accuracy

Face Recognition is the ability to find and detect a person by their facial attributes. Face is a multi dimensional and thus requires a considerable measure of scientific calculations. Face recognition system is very useful and important for security, law authorization applications, client confirmation and so forth. Hence there is a need for an efficient and cost effective system. There are numerous techniques that are as of now proposed with low Recognition rate and high false alarm rate. Hence the major task of the research is to develop face recognition system with improved accuracy and improved recognition time. Our objective is to implementing Raspberry Pi based face recognition system using conventional face detection and recognition techniques such as A Haar cascade classifier is trained for detection and Local Binary Pattern (LBP) as a feature extraction technique. With the use of the Raspberry Pi kit, we go for influencing the framework with less cost and simple to use, with high performance. 

Face Recognition System Based on Raspberry Pi Platform

2019 ◽  
Author(s):  
Nafis Mustakim ◽  
Noushad Hossain ◽  
Mohammad Mustafizur Rahman ◽  
Nadimul Islam ◽  
Zayed Hossain Sayem ◽  
...  
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Raspberry Pi ◽  
Face Recognition System

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 Face Recognition with OPENCV and Smart Shopping in Cloud Computing

2019 ◽  
Vol 9 (1) ◽  
pp. 4078-4082
Keyword(s):  
Cloud Computing ◽  
Face Recognition ◽  
Human Life ◽  
Raspberry Pi

The present data time targets digitizing data and executing effective, instinctive and easy to understand frameworks to unravel human life. Making a canny truck that deals with quick charging is a jump towards an advanced and totally computerized shopping knowledge. Buying thing in enormous supermarkets with a gigantic assortment of things is time taking procedure. It can be optimized through motorizing the charging framework. A shopping truck contains a versatile computational gadget (like raspberry pi) and a customized thing recognizable proof innovation (like the radio recurrence distinguishing proof innovation). Minute charging without long lines at counters and monitoring consumption constant are the two goals of this canny truck. This paper depends on building up a venture through the intend to lessen point in time used up on shopping of regular things and make the procedure less repetitive. Besides, it empowers the customers to use their point in time on other gainful and increasingly huge exercises.

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 Penerapan Liveness Sebagai Anti-Spoofing Citra Digital Pada Sistem Keamanan Akses Kontrol Ruang Server Berbasis Raspberry Pi

2021 ◽  
Vol 16 (2) ◽  
pp. 31
Author(s):  
Galih Rizkya Safri ◽  
Denny Irawan ◽  
Rini Puji Astutik
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Local Binary Pattern ◽  
Convolution Neural Network ◽  
Raspberry Pi

Ruang server merupakan ruang yang menyimpan aset-aset dan data-data penting dari suatu perusahaan sehingga keamanan untuk akses keluar masuk ruang server perlu diperhatikan agar menghindari kejadian yang tidak diinginkan. Pada saat ini sudah banyak dikembangkan sistem keamanan hingga kunci konvensional, RFID, serta sistem keamanan menggunakan teknologi biometrik seperti sidik jari, iris, dan juga wajah yang memiliki karakteristik berbeda setiap wajahnya sehingga diharapkan bisa menjadi sistem keamanan yang handal. Seiring berkembangnya teknologi membuat seseorang semakin mudah mengakses internet untuk mendapatkan data-data biometrik seperti wajah yang dapat di gunakan untuk pemalsuan atau spoofing untuk mendapatkan akses ilegal ke suatu ruangan. Penelitian sistem keamanan ini menggunakan pegenalan wajah (face recognition) dan liveness sebagai anti- spoofing dan metode Local Binary Pattern dan Convolution Neural Network untuk meningkatkan sistem keamanan agar terhindar dari pemalsuan wajah. Hasil penelitian ini mendapatkan keakuratan pendeteksian wajah asli atau palsu sebesar 90% dan akurasi sistem dalam mengenali wajah sebesar 93.3%. Kesalahan proses pengenalan wajah terjadi 5 kali dan kesalahan saat proses pengenalan wajah dan 2 kali saat pengenalan wajah asli, dari 4 skenario dengan 40 kali uji coba. Sistem keamanan pada penelitian ini 95% bekerja dengan baik dan sesuai dengan perencanaan

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