scholarly journals Face Recognition of Passenger for Bus Services

2021 ◽  
Author(s):  
Mohammad Azerul Azlan ◽  
◽  
Abd Kadir Mahamad ◽  
Sharifah Saon ◽  
◽  
...  
Keyword(s):  
Face Recognition ◽  
Internet Of Things ◽  
University Students ◽  
Recognition System ◽  
Raspberry Pi ◽  
Counting System ◽  
Face Classification ◽  
Face Recognition System ◽  
Bus Services

Most university students are using the bus provided by the university's management to move from one place to another place. The analysis are required to improvise the quality of the of bus services such as the amount of passenger that using the bus and information of passengers such as gender. The objectives of this project are to develop face recognition system based on gender using Raspberry Pi 4 and Intel Neural Compute Stick 2 and to test and validate the performance of the developed system for face classification and passenger counting system. Also this system is able to store passenger information into Google Firebase Cloud with Internet of Things. This system is used Raspbian in Raspberry Pi 4 with the libraries that used for face classification and recognition such as OpenCV and OpenVINO. This project able to detect faces of the passengers soon as they ride the bus and determine gender of the passengers and count passengers according gender and the information of the passengers will stored in Google Firebase. There are some recommendation that need to be added in this project to improve efficiency of the system.

scholarly journals A Review of Face Recognition System Using Raspberry Pi in the Field of IoT

10.29007/1xq9 ◽  
2018 ◽  
Author(s):  
Arihant Kumar Jain ◽  
Richa Sharma ◽  
Anima Sharma
Keyword(s):  
Face Recognition ◽  
Internet Of Things ◽  
Urban Communities ◽  
Recognition System ◽  
Raspberry Pi ◽  
Infrared Sensor ◽  
Security Framework ◽  
Security Applications ◽  
Face Recognition System ◽  
A Charge

In these days circumstance, the security frames the most essential segment of our lives. Security of the house or the close what's more, dear ones is critical to everyone. Home computerization is an energizing zone for security applications. This field has improved with new advances such Internet of things (IoT). In IoT, each device carries on as a little piece of a web hub and each hub associate and convey. Of late, surveillance cameras are used keeping in mind the end goal to construct security spots, homes, and urban communities. Be that as it may, this innovation needs a man who recognizes any issue in the edge taken from the camera. In this paper, an Internet of Things is joined with PC vision so as to identify the characteristics of individuals. For this reason, to execute this framework, a charge card measure PC that uses its own particular camera board for the security framework, for example, raspberry pi 3 used. In like manner, Passive Infrared Sensor (PIR) mounted on the Raspberry PI is used to identify any developments. So it screens and gets warnings when movement is distinguished, catches the picture and identify the faces, at that point sends pictures to a Smartphone by means of using wire application. Internet of things in light of wire application used to see the action and get sees when development is distinguished.

scholarly journals Real-time Triplet Loss Embedding Face Recognition for Authentication Student Attendance Records System Framework

2021 ◽  
Vol 5 (2) ◽  
Author(s):  
Hady Pranoto ◽  
Oktaria Kusumawardani
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Success Factors ◽  
Recognition System ◽  
Expression Recognition ◽  
Student Attendance ◽  
Face Recognition System ◽  
Triplet Loss ◽  
Education Process

The number of times students attend lectures has been identified as one of many success factors in the learning process in many studies. We proposed a framework of the student attendance system by using face recognition as authentication. Triplet loss embedding in FaceNet is suitable for face recognition systems because the architecture has high accuracy, quite lightweight, and easy to implement in the real-time face recognition system. In our research, triplet loss embedding shows good performance in terms of the ability to recognize faces. It can also be used for real-time face recognition for the authentication process in the attendance recording system that uses RFID. In our study, the performance for face recognition using k-NN and SVM classification methods achieved results of 96.2 +/- 0.1% and 95.2 +/- 0.1% accordingly. Attendance recording systems using face recognition as an authentication process will increase student attendance in lectures. The system should be difficult to be faked; the system will validate the user or student using RFID cards using facial biometric marks. Finally, students will always be present in lectures, which in turn will improve the quality of the existing education process. The outcome can be changed in the future by using a high-resolution camera. A face recognition system with facial expression recognition can be added to improve the authentication process. For better results, users are required to perform an expression instructed by face recognition using a database and the YOLO process.

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

Quality Estimation for Facial Biometrics

2020 ◽  
pp. 298-320
Author(s):  
Sanjida Nasreen Tumpa ◽  
Andrei Dmitri Gavrilov ◽  
Omar Zatarain Duran ◽  
Fatema Tuz Zohra ◽  
Marina L. Gavrilova
Keyword(s):  
Face Recognition ◽  
Facial Recognition ◽  
Linear Regression Analysis ◽  
Estimation Method ◽  
Recognition System ◽  
Quality Factors ◽  
Quality Estimation ◽  
Video Frames ◽  
Face Recognition System

Over past decade, behavioral biometric systems based on face recognition became leading commercial systems that meet the need for fast and efficient confirmation of a person's identity. Facial recognition works on biometric samples, like image or video frames, to recognize people. The performance of an automated face recognition system has a strong relationship with the quality of the biometric samples. In this chapter, the authors propose a quality estimation method based on a linear regression analysis to characterize the relationship between different quality factors and the performance of a face recognition system. The regression model can predict the overall quality of a facial sample which reflects the effects of various quality factors on that sample. The authors evaluated the quality estimation model on the Extended Yale Database B, finally formulating a data set of samples which will enable efficient implementation of biometric facial recognition.

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 Comparative analysis of face recognition systems built using standard architecture blocks

Telecom IT ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 94-101
Author(s):  
E. Kalyashov
Keyword(s):  
Face Recognition ◽  
Comparative Analysis ◽  
Original Data ◽  
Recognition System ◽  
Practical Relevance ◽  
Research Subject ◽  
Face Recognition System ◽  
Recognition Quality ◽  
Recognition Systems

Research subject. The article reviews ways of constructing face recognition systems based on standard modules. Method. The study is based on comparison of performance and recognition quality of various pipelines. Core results. Values of reached recognition quality and dependencies from a type of original data are presented. Practical relevance. The results could be used while implementing various face recognition system pipelines.

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