Student Attendance Management System by Using Fingerprint Reader

International Research Journal of Engineering and Technology (IRJET)

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Student Attendance Management System by Using Fingerprint Reader Swasti Saha Roy1, Aditya Kumar Gupta2, Aman Kumar3, Sukhminder Singh4 AIT-CSE, Chandigarh University, Gharuan, Punjab, INDIA Jagjit Singh, AIT-CSE, Chandigarh University, Gharuan, Punjab, INDIA ---------------------------------------------------------------------***---------------------------------------------------------------------

Abstract - The "Student Attendance Management System

advancements aim to not only automate attendance tracking but also provide valuable insights, enhance security, and promote efficient communication within educational environments.

Using Fingerprint Recognition" project presents an innovative and efficient solution to the traditional method of recording and managing student attendance in educational institutions.

This project leverages modern biometric technology to streamline the attendance tracking process, minimize manual errors, and enhance security. Traditional attendance systems often involve manual processes that are time-consuming and prone to errors. In contrast, our project employs fingerprint recognition technology to identify and record student attendance accurately and conveniently. Each student's fingerprint serves as a unique identifier, eliminating the possibility of proxy attendance and ensuring data integrity.

Throughout this project, we will delve into the technical aspects of fingerprint recognition, database management, and user interface design to develop a comprehensive solution that meets the unique needs of educational institutions.

2.LITERATURE REVIEW Singh et. al. (2017) [1]: This paper claims that the system's efficiency improvement through automation, real-time monitoring capabilities, and the integration of RFID and GSM technologies to create a robust solution for attendance tracking. The authors may discuss how their proposed system reduces administrative burdens, enhances communication, and provides an innovative approach to attendance management in educational institutions and achieves the highest accuracy of 89.35%.

Keywords—Fingerprint recognition, Attendance tracking, Data integrity, Data security, Eliminating proxy attendance, Biometric attendance, Database management, User friendly interface, Automation, Attendance analytics.

1.INTRODUCTION In the ever-evolving landscape of educational institutions, managing and accurately tracking student attendance is of paramount importance. Effective attendance control is essential for maintaining responsibility as well as improving the quality of education in general. Conventional attendance systems frequently rely on labor-intensive, manually processed procedures that can be inaccurate, timeconsuming, and susceptible to fraudulent activities like proxy attendance. With the goal of addressing these issues and ushering in a new era of efficient attendance tracking, our project presents the "Student Attendance System Using Fingerprint Reader."

Sharma et. al. (2018) [2]: This paper presented a completely programmed technique and claims the system's ability to automate the attendance tracking process, reducing manual effort and improving accuracy. The integration of RFID and GSM is likely to be highlighted for its role in real-time data transmission and efficient communication. The authors may emphasize the practicality and effectiveness of their proposed system as a solution for streamlining attendance management in educational settings.

Gupta et. al. (2019) [3]: This work presents an innovative system for attendance management in educational institutions. The paper may claim that their proposed system leverages biometric technology and IoT to create an advanced attendance management solution. Potential detailed claims may include the accuracy and security enhancements achieved through biometric authentication, the real-time monitoring capabilities facilitated by IoT devices, and the overall efficiency improvement in the attendance tracking process.

Widespread praise has been bestowed upon biometric technology, especially fingerprint recognition, for its accuracy, security, and speed in confirming an individual's identity. Our project intends to transform the way attendance is tracked and managed in educational institutions by utilizing the power of biometrics.

This project's primary objective is to develop an efficient, user-friendly, and secure system that simplifies the process of recording and monitoring student attendance. To further improve and expand its capabilities, future work includes the integration of multiple biometric modalities, real-time monitoring, mobile application support, machine learning for anomaly detection, and cloud-based scalability. These

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Chen et. al. (2020) [4]: This paper suggests that the paper likely explores the development of a smart campus solution using Internet of Things (IoT) technology for student attendance management. Potential detailed claims could include the implementation of IoT devices for real-time

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3.PROPOSED METHODOLOGY

monitoring of attendance, the integration of smart sensors for accurate data collection, and the creation of an efficient and automated attendance management system.

The proposed methodology for a project on student attendance using a fingerprint reader should outline the steps and procedures we intend to follow to design, develop, and implement the system. Here's a comprehensive methodology for the project:

Biswaranjan et. al. (2021) [5]: This paper claims to present a novel system for student attendance management using wireless biometric fingerprint technology. The authors likely propose an automated solution that employs deep learning techniques integrated with the Internet of Things (IoT). The paper may detail the design and implementation of this system, emphasizing its potential to streamline attendance processes, enhance efficiency, and contribute to the broader field of deep learning and IoT applications in educational settings.

1.

Requirement analysis: Define the specific requirements of the attendance system in collaboration with stakeholders, including administrators, educators, and students. Identify hardware and software prerequisites.

2.

System design: Develop a system architecture that outlines the components and their interactions. Design the database schema for storing student information and attendance records. Define the user interface design to ensure it is intuitive and user-friendly for both students and faculty.

Zhang et. al. (2018) [6]: The paper includes the detailed development and implementation of a system capable of accurately tracking student attendance through facial recognition. It includes the system's ability to enhance efficiency, reduce manual effort, and provide a robust and automated solution for attendance management. The use of deep learning in facial recognition suggests a focus on sophisticated algorithms for improved accuracy and adaptability.

3.

Fingerprint data collection: Acquire the fingerprint data of students who will be using the system. Ensure data privacy and security measures are in place for the collection and storage of biometric data, adhering to legal and ethical standards.

Table -1: Literature Survey Table Sl. No.

TITLE

Year and Citation

Article/ Author

Technique and Evaluation Parameter

1.

A Comprehensive Study on Student Attendance Management System using RFID and GSM Technology.

(2017)

Singh, A., & Verma, A.

RFID and GSM Technology

An RFID and GSM-based Automatic Student Attendance System.

(2018)

Biometric-Based Student Attendance Management System using IoT.

(2019)

Smart Campus: An IoT-based Approach for Student Attendance Management.

(2020)

Automated Wireless Biometric Fingerprint Based Student Attendance System.

(2021)

2.

3.

4.

5.

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[1]

4.

Fingerprint Recognition Algorithm: Choose or develop a fingerprint recognition algorithm that suits the project's requirements. Implement the algorithm for fingerprint enrolment and verification.

5.

System development: Develop the software components of the attendance system, including the user interface, fingerprint recognition module, and real-time monitoring features.

and Accuracy 86.2%

[2]

Sharma, R., & Dhillon, B. S.

–

6.

Integration: Integrate the fingerprint reader hardware with the software system. Test the integration to ensure accurate data capture and verification.

RFID and GSM Technology and Accuracy 89.35%

[3]

[4]

Gupta, R., & Srivastava, M..

IoT

Chen, J., & Wang, L.

IoT-based Approach

–

7.

User training: Conduct user training sessions for administrators, educators, and students to ensure they understand how to use the system effectively and securely.

and Accuracy 85.9%

8.

Testing and validation: Perform rigorous testing, including unit testing, integration testing, and user acceptance testing, to identify and rectify any issues. Validate the system's accuracy and reliability through extensive testing scenarios.

–

and Accuracy 87.11% [5]

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Biswaranjan S., Jayshree H., Siddharth S. et. al.

9.

Security Measure: Implement security measures to protect the system against unauthorized access and data breaches. Ensure compliance with data protection regulations and ethical guidelines.

–

Deep Learning with IoT and Accuracy 92.6%

Impact Factor value: 8.226

10.

Data backup and recovery: Set up regular data backup procedures to prevent data loss. Develop a data recovery plan in case of system failures or data corruption.

–

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11.

Deployment: Deploy the system in a controlled environment initially to monitor its performance. Gradually roll out the system to different classrooms or areas of the educational institution.

Class Information: The specific classes or courses the student is registered for, including course codes and titles.

14.

Monitoring and maintenance: Establish a system for continuous monitoring to identify and resolve any operational issues. Provide ongoing maintenance and support to address software updates, hardware maintenance, and user inquiries.

Date: The date on which the attendance is recorded.

2.

Attendance Record:

Class/Session ID: A unique identifier for each class or session.

Student ID: The unique identifier linking the student to their attendance record.

15.

User Feedback and Iteration: Collect feedback from users and stakeholders to identify areas for improvement.

Iterate on the system based on feedback to enhance usability and functionality.

Attendance Status: A binary indicator (e.g., "Present" or "Absent") denoting the student's attendance status for that class or session.

By following this proposed methodology, the project can be systematically developed and implemented, by ensuring a robust, accurate, and secure attendance management system for educational institutions.

Time Stamp: The timestamp indicating when the attendance was marked.

3.

Course and class information:

Course Code: A unique code assigned to each course.

Course Title: The full title or name of the course.

Instructor/Teacher: The name or ID of the instructor or teacher responsible for the course.

Class Schedule: The schedule of classes, including start times, end times, and days of the week.

4.

Fig -1: Workflow of the model

Fingerprint Template: If the system uses fingerprint recognition, this field would store the unique fingerprint template for each student.

4.EXPERIMENTAL RESULTS A. Dataset Description

Fingerprint Enrollment Date: The date when the student's fingerprint was enrolled in the system.

Creating a dataset for a student attendance management system involves collecting and organizing relevant data that the system will use to record and manage student attendance.

5.

Department: The department or academic division to which the student is affiliated.

Student Information:

6.

Student ID: A unique identifier for each student in the system.

Password: The password or authentication credentials for accessing the system.

Contact Information: Email address, phone number, and any other relevant contact details.

7.

Course Information: The program or courses in which the student is enrolled.

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Impact Factor value: 8.226

User Account Information:

Username: The username associated with the student's account for accessing the attendance system.

Student Name: The full name of the student.

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Academic Information:

Grade/Year Level: The academic year or grade level to which the student belongs.

Below is a description of the key components of a student attendance dataset: 1.

Fingerprint or Biometric data:

Security And Privacy Fields:

Data Encryption: Indicate whether data, especially biometric data, is encrypted for security.

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Access Controls: Specify who has access to view and modify the data, as well as any authentication mechanisms in place.

i)

Report on the effectiveness of user training programs and how they contributed to user acceptance and adoption of the system.

B. Evaluation Parameter Here are some key aspects to consider when presenting experimental results: a)

j)

When presenting experimental results, use charts, graphs, and tables to visualize data, and provide clear explanations and interpretations of the findings.

Efficiency:

C.

Present data on the time savings achieved by using the fingerprint attendance system compared to manual methods. This can include the time it takes for students to mark their attendance and for instructors to record and manage attendance. c)

Results Visualization

Usability:

Include feedback from students, instructors, and administrative staff regarding the system's usability.

Conduct surveys or interviews to gather qualitative data on user satisfaction during the testing phase and how they were addressed. d)

Recommendations for Improvement:

Based on the experimental results and feedback, provide recommendations for further improvements or enhancements to the system.

Accuracy:

Report the accuracy of the fingerprint recognition system in correctly identifying and verifying students. This can be measured in terms of the True Positive Rate (TPR), False Positive Rate (FPR), and the overall accuracy rate. b)

User Training and Acceptance:

Fig -2: Attendance Data

Security and Data Privacy:

Discuss the security measures in place to protect biometric data and ensure its privacy. Evaluate the system's resilience to potential attacks or unauthorized access. e)

System Reliability:

Present data on the system's reliability, including its uptime, availability, and performance under various environmental conditions. f)

Scalability:

Fig -3: Input Data

Evaluate the system's scalability by testing it with different numbers of students and faculty members. g)

Data Analysis:

Analyze the attendance data collected by the system over time including the trends, patterns, and correlations that may provide insights into student attendance behaviour. h)

Comparative Analysis:

Compare the fingerprint-based attendance system with traditional methods, highlighting the advantages and disadvantages of each approach. Consider cost-effectiveness and resource savings in your comparison.

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Fig -4: Sample Fingerprint

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5.CONCLUSION AND FUTURE WORK

REFERENCES

A. Conclusion In conclusion, the development and implementation of the Student Attendance Management System represent a significant step forward in modernizing attendance tracking and management within educational institutions. This project aimed to streamline the often cumbersome and error-prone process of attendance recording, ensuring accuracy, efficiency, and enhanced data security.

Through rigorous research, design, and development efforts, we have successfully created a robust system that leverages biometric fingerprint recognition technology to revolutionize the way attendance is recorded. The system encompasses a user-friendly interface, real-time monitoring capabilities, and a secure database management system, all of which contribute to its effectiveness and reliability.

However, it's important to acknowledge that no project is without its challenges and limitations. Some of the challenges faced during this project include the need for specialized hardware (fingerprint readers), initial setup and enrolment of student data, and potential technical issues that may arise during system operation. Ongoing maintenance and support will be crucial to address these challenges.

[1]

Singh, A., & Verma, A. (2017). "A Comprehensive Study on Student Attendance Management System using RFID and GSM Technology." In 2017 International Conference on Computing, Communication and Automation (ICCCA).

[2]

Sharma, R., & Dhillon, B. S. (2018). "An RFID and GSMbased Automatic Student Attendance System." In 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT).

[3]

Gupta, R., & Srivastava, M. (2019). "Biometric-Based Student Attendance Management System using IoT." In 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS).

[4]

Chen, J., & Wang, L. (2020). "Smart Campus: An IoTbased Approach for Student Attendance Management."

In 2020 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData).

[5]

B. Swain, J. Halder, S. Sahany, P. P. Nayak and S. Bhuyan, "Automated Wireless Biometric Fingerprint Based Student Attendance System," 2021 1st Odisha International Conference on Electrical Power Engineering, Communication and Computing Technology (ODICON), Bhubaneswar, India, 2021, pp. 17, doi: 10.1109/ODICON50556.2021.9428983.

[6]

A. Jain and S. Dey, "A review on student attendance management system using machine learning techniques," 2020 International Conference on Smart Electronics and Communication (ICOSEC), 2020, pp.

324-328.

[7]

Ahirwar, N., Tiwari, D., Sharma, R., & Bansal, S. (2020).

An Intelligent System for Student Attendance Management Using Face Recognition. In Advances in Signal Processing and Intelligent Recognition Systems (pp. 95-106). Springer, Singapore.

[8]

Bhagwat, R., Zade, D. G., & Salodkar, A. (2021).

Attendance management system using face recognition.

International Journal of Advanced Trends in Computer Science and Engineering, 10(1), 51-56.

[9]

Bhowmick, R., & Singh, D. (2021). An efficient facial recognition-based automated attendance management system using machine learning. Multimedia Tools and Applications, 1-23.

In closing, the success of this project underscores the importance of embracing technology to enhance educational processes. We believe that the “Student Attendance Management System” will have a positive and lasting impact on educational institutions, benefiting students, educators, and administrators alike.

B. Future Work 

Cloud Integration: Move the attendance system to a cloud-based platform for scalability, accessibility, and ease of maintenance. This allows for easy updates and ensures data security.



User Authentication and Authorization: Implement robust user authentication and authorization mechanisms to ensure that only authorized personnel can access and modify attendance data.



Machine Learning for Anomaly Detection: Integrate machine learning algorithms to detect anomalies in attendance patterns. This can help identify unusual attendance behavior, such as proxy attendance or inconsistent patterns.



Real-time Monitoring: Implement real-time monitoring of attendance to provide instant updates to teachers and administrators. This can help in identifying and addressing attendance issues promptly.

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[10] Chaudhary, A., Bisht, S., & Bhandari, P. (2020). QR code-

based

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student attendance management

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International Journal of Advanced Trends in Computer Science and Engineering, 9(5), 5231-5236.

[11] Choudhary,

P., & Singh, K. K. (2019). Automated attendance management system using QR code.

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M., Kanchan, A., & Dey, N. (2019). Smart attendance management system using Bluetooth Low Energy (BLE). 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 1-5.

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student attendance management system using IoT and RFID. 2020 International Conference on Smart Electronics and Communication (ICOSEC), 573-577.

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