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ai mini project faceid program for timekeeping management

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Tiêu đề FaceID Program for Timekeeping
Tác giả Trâân Dương Thanh Phong, Vũ Bảo Khang, Đoàn Nhật Minh, Đặng Trúc Quỳnh, Trâân Nguyêên Thiên Vũ
Người hướng dẫn Nguyen The Dai Nghia, Lecturer
Trường học University of Economics and Law
Chuyên ngành Information Systems
Thể loại Mini Project
Định dạng
Số trang 11
Dung lượng 2,03 MB

Nội dung

Our project aims to explore how we can use FaceID technology for timekeeping purposes in class, offering users greater accuracy, security, and convenience, It eliminates the need for phy

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UNIVERSITY OF ECONOMICS AND LAW

FACULTY OF INFORMATION SYSTEMS

AI Mini Project: FaceID Program for

Timekeeping Management

Lecturer: Nguyen The Dai Nghia Class: 222MI5217

Team: 2

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Table of Contents

}L FacelD Program for Timekeeping

Introduction

Overview

Application

Limitations

Recommendation

Conclusion

Il, Team Management

1 Trello

2 Group assessment

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LIst of Tables

Table 1 Group Assessment

List of Figures

Higưre 1 FacelD Sample

Figure 2 CSV File Sample

Figure 3 On-processing Dashboard

Figure 4 Task descriptions

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I FaceID Program for Timekeeping

1 Introduction

Face recognition is a type of biometric identification that extracts and stores unique facial features to identify a person Its broad application has led to a booming market, estimated at $4.4 billion in 2019 and projected to rise to $10.9 billion by 2025 China is one country where this technology is widespread, used for unlocking devices, authorizing transactions, and timekeeping Our project aims to explore how we can use FaceID technology for timekeeping purposes in class, offering users greater accuracy, security, and convenience, It eliminates the need for physical timecards or passwords and provides better data management, as all data is stored electronically We aim to develop a solution for automated attendance using face recognition in a corporate setting, replacing manual approaches

2 Overview

Teachable Machine is a web-based platform that allows users to build and train machine learning models without the need for extensive coding knowledge With Teachable Machine, businesses can easily create models that can recognize faces and link them to employee records for timekeeping management According to a case study

by Google, Teachable Machine has been used to develop facial recognition models for various applications, including identifying people with visual impairments and monitoring wildlife populations

Facial recognition technology is becoming increasingly popular in various industries With the ability to automate attendance tracking and improve accuracy, businesses are exploring using facial recognition technology to streamline their timekeeping processes The global market for biometric technology, including facial recognition, is expected to reach $59.31 billion by 2025, according to a report by MarketsandMarkets While there are limitations and ethical considerations to consider, facial recognition technology remains a promising tool for timekeeping management

3 Application

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FaceID technology has revolutionized how timekeeping management is done in universities by making it easier and more efficient for universities to track attendance and manage timekeeping records of students and faculty members It accurately identifies individuals by their unique facial features, eliminates the possibility of proxy attendance or time theft, and provides real-time data to monitor attendance and track its movement This data can be used to identify patterns of attendance, improve the performance of students or faculty members, and make informed decisions about academic programs and resource allocation As a result, FaceID has improved the efficiency of educational programs, reduced administrative burdens, and improved student outcomes This saves time and reduces the risk of infectious disease transmission, especially during pandemics like COVID-19

Name 99%

Figure 1 FacelD Sample

To employ FacelD for attendance in the classroom, we first collected faces through the webcam on a Teachable Machine to compile a data set Besides, use Python to detect faces by scanning each pixel on the picture to calculate a confidence score (assuming 95% confidence) and store daily attendance data in CSV format And lastly, export a CSV file for information management

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Name, Time

khang,21:56:59

Figure 2 CSV File Sample

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4 Limitations

Although FacelD ís a popular cholce for tíimekeeping management, ít has some limitations that classes should be aware of before implementation One of the significant limitations is the potential for low accuracy caused by factors such as changes in facial appearance or lighting conditions in the workplace In such cases, the system may fail to recognize the students’ faces, leading to delays in timekeeping and attendance records Another limitation of FacelD is the need for an existing database

of students’ images This can be time-consuming to collect and create, requiring careful management to ensure the database remains up-to-date Additionally, if a new student

or visitor needs to be added to the system In that case, it may not be possible to register their face without manual intervention, which can further delay the process FaceID remains a popular choice for timekeeping management despite these limitations due to its ease of use and reliability However, it is crucial for organizations

to consider the potential drawbacks before implementation carefully and to develop a plan for managing these limitations to ensure the system's continued success

5 Recommendation

FaceID Keep Track is a revolutionary facial recognition technology enabling users to access their devices quickly and securely There are several recommendations to consider to ensure that the face recognition system is as accurate as possible These include using high-resolution images, employing advanced biometric techniques such

as 3D facial recognition, and regularly testing and calibrating the system to detect any changes in accuracy Moreover, to improve its performance, it is essential to have an optimal database for the storage and management of the data This can be done by making sure that the database is regularly updated with new information and properly organized for easy retrieval of data Finally, it is recommended to add a face registration function in order for people to be able to enter the necessary departments This would allow for more secure access and better tracking of personnel movements within the organization Additionally, it would also help in preventing unauthorized persons from entering restricted areas By implementing these recommendations, the FaceID Keep Track system can be further improved and even more effective at providing secure and convenient access to our devices

6 Conclusion

FacelD for timekeeping management on Teachable Machine is a promising technology that can help universities streamline their timekeeping processes However, it Is essential to consider the limitations and recommendations mentioned above to ensure successful implementation and protect students’ privacy It is also important to note

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that while FaceID may be a valuable tool for timekeeping management, there should

be other methods for tracking students' attendance and productivity Universities should also consider other methods, such as manual record-keeping or biometric systems that do not rely solely on facial recognition technology In conclusion, implementing FacelD for timekeeping management on Teachable Machine can bring efficiency and accuracy to the organization's timekeeping processes Still, it is essential

to consider the limitations and recommendations to ensure successful implementation and protect users’ privacy As with any new technology, users should carefully evaluate the benefits and drawbacks of using facial recognition technology for timekeeping management before implementing it in their workplace

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Il Team Management

1 Trello

fern

toa

ko

Figure 3 On-processing Dashboard

& Model evaluation

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"= Activity

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Figure 4 Task descriptions

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2 Group assessment

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pie badtoS- | Very bad to 5

- Writing Python code

1 K214111977 Thanh Ph g ~ Modeling evaluation 100% 5 5

- Categorizing the dataset

2 K214114980 | 747 Nguyen | Limitations 100% 5 5

- Writing Python code

3 K214110862 |VũBảoKhang |" raining the model - Mode! evaluation 100% 5 5

- Testing the model

- Collecting the Dataset

- Formatting the report

5 K214110868 |Qỗng Trúc - Conclusion 100% 5 5

y - Designing PPT slides

Table 1 Group Assessment

Ngày đăng: 22/08/2024, 17:03

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