QR Code‑Based Attendance System Using Deep Learning

P. Jacob Vijaya Kumar

, Patan Madarvali

, Basapuram Mahesh

, Jammana Govardhan Reddy

Chakali Mahidhar

and Dwaram Satish Reddy

Department of Computer Science and AI/ML, Santhiram Engineering College, Nandyal‑518501, Andhra Pradesh, India

Department of Computer Science and Design, Santhiram Engineering College, Nandyal‑518501, Andhra Pradesh, India

Keywords: QR Code, Attendance System, Deep Learning, Convolutional Neural Networks (CNNs), Image Processing,

Automation.

Abstract: Integrating QR code generation with deep gaining knowledge of has revolutionized attendance control

structures. This paper offers a complicated QR code-based attendance machine augmented with deep

mastering strategies to enhance accuracy, scalability, and efficiency. The gadget leverages QR codes for

speedy, contactless statistics retrieval, even as deep mastering models address challenges along with QR code

deformation, terrible lights conditions, and real-time processing. The CNNs are used to detect the QR code

and improve accuracy, even under suboptimal conditions. In addition, a user -friendly interface ensures

spontaneous operations for both administrators and participants.

1 INTRODUCTION

Traditional attendance systems face demanding

situations, including manual errors, time inefficiency,

and scalability barriers. QR code-primarily based

systems provide a contactless, fast, and automatic

opportunity. However, conventional QR scanners

battle with distortions, low lights, or partial

obstructions. By integrating deep studying, this

gadget enhances robustness, ensuring reliable

performance in various situations.

2 LITERATURE REVIEW

2.1 QR Codes for Taking Attendance

QR codes are those square barcodes you scan with

your phone. They're popular for attendance because

they're:

• Simple to use

• Quick to process

• Able to hold lots of information

But basic QR attendance systems have some

problems:

• People can cheat by creating fake QR codes

• They struggle with large groups

• They don't work well in poor lighting or when

the code is at an odd angle

That's where AI comes in to help solve these issues.

2.2 How AI Improves These System

Researchers are using advanced AI techniques to

make QR attendance systems better:

• Better QR Code Reading: AI models like YOLO

can find and read QR codes quickly, even in bad

lighting or when the code is distorted.

• Catching Cheaters: AI can spot fake QR codes

by finding tiny details that humans might miss.

• Double-Checking Identity: Some systems pair

QR codes with facial recognition - you scan the

code, then the system checks your face to make

sure it's you.

2.3 Recent Breakthroughs

• One research team created a system that uses

both QR codes and facial recognition, making

it much harder to cheat.

Kumar, P. J. V., Madarvali, P., Mahesh, B., Reddy, J. G., Mahidhar, C. and Reddy, D. S.

QR Code-Based Attendance System Using Deep Learning.

DOI: 10.5220/0013883300004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 2, pages

383-388

ISBN: 978-989-758-777-1

383

• Another team developed a super-fast QR code

detector that works well even when people are

moving around.

• A third group built an AI system that can spot

tampered QR codes by examining unusual

patterns in the pixels.

2.4 Current Challenges and What's

Despite this progress, some hurdles remain:

• Processing Power: AI requires serious

computing power, which can be a problem for

real-time applications.

• Training Data: Teaching AI to recognize all

possible QR code variations requires lots of

example data.

• Connected Devices: Future systems might

connect QR attendance with smart devices for

seamless tracking.

2.5 Researchers Are now Working on

• Creating lighter AI models that don't need as

much computing power

• Developing methods that protect privacy

while improving security

• Combining QR codes with fingerprints or

voice recognition for extra security.

3 EXISTING SYSTEMS

3.1 Old-School QR Code Attendance

Problems

• People can cheat by using fake QR codes

• Doesn't work well in poor lighting or weird

angles

• Just scanning a code doesn't prove it's you

3.2 QR Systems with Brain QR Codes

that Recognize Your Face

How it works: Scan your QR code, and then the

system checks your face to make sure it's you.

Cool features:

• Uses smart computer vision to match your face

with your stored photo

• Much harder to cheat the system

Drawbacks:

• Needs powerful computers to work quickly

• Struggles in dark rooms

3.3 Super-Fast QR Code Spotting How

It Works

Uses a smart technology called YOLO that can find

and read QR codes instantly, even when they're

moving.

Cool features:

• Can spot and process many QR codes at once

• Works well even in challenging conditions

Drawbacks:

• Needs a gaming-level graphics card to run

smoothly

• Still has trouble with badly damaged QR codes

3.4 Fake QR Code Detective

How it works: The system learns to spot tiny

differences between real and fake QR codes that

humans can't see.

Cool features:

• Catches people trying to use fake codes

• Makes the whole system more trustworthy

Drawbacks:

• Needs to study thousands of fake and real codes

to learn the difference

• Uses lots of computing power

3.5 Connected Smart Attendance

How it works: Combines QR codes with internet-

connected devices and AI to track attendance in real

time.

Cool features:

• Attendance data gets stored in the cloud

instantly

• Can show patterns like who's usually late or

which days have poor attendance

Drawbacks:

• Needs a good internet connection

• Costs more to set up initially

3.6 Triple-Check Security

How it works: After scanning your QR code, you

also need to verify your fingerprint or voice.

Cool features:

• Extremely secure nearly impossible to cheat

• Perfect for high-security places

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

COMMUNICATION, AND COMPUTING TECHNOLOGIES

384

Drawbacks:

• Requires extra hardware like fingerprint

scanners

• More complicated and expensive

• TopComparison of Existing Systems

(Table 1)

Table 1: Comparison of Existing Systems.

System Key Features Advantages Limitations

Hybrid QR Code + Face

Recognition

Combines QR code

scanning with face

reco

nition.

High accuracy, robust

against spoofing.

Computationally intensive,

performance degrades in

oor li

htin

Tampered QR Code

Detection

Detects fake QR codes

using deep learning.

Enhances security, and

high accuracy.

Requires large dataset,

computationally intensive.

IoT-Integrated System

Combines QR codes with

IoT for real-time data

rocessing.

Scalable, provides real-

time analytics.

Requires stable internet,

high setup cost.

Multi-Factor

Authentication

Adds biometric

authentication (e.g.,

fingerprint, voice) to QR

code scanning.

Highly secure, and

suitable for high-security

environments.

Requires additional

hardware, increased

complexity, and cost.

4 METHODOLOGY

4.1 Problem Definition

To accomplish the objectives outlined in advance, a

dependent step-through-step method has been

implemented.

The methodology is defined as follows:

Develop a QR Code Generator Android

Application: Create an Android app that generates

QR codes the usage of student-specific records

including roll wide variety and scholar ID.

Develop an Attendance Management Android

Application: Build an Android app capable of

scanning QR codes to file attendance for precise

topics and routinely generate attendance sheets based

at the accumulated statistics.

4.2 Software Requirements

• Development Tools: Android Studio (for

cellular app improvement)

• Python (for deep mastering version education

and backend processing)

• MS Excel

4.3 Hardware Requirements

Android Smartphone.

4.4 Inputs / Outputs

• Inputs: QR codes, and Bar codes

• Outputs: Excel sheets

Database usage

MYSQL Database

4.5 Use Case

The machine involves more than one actors, each

with precise roles and functionalities. These actors

and their interactions inside the QR code-primarily

based attendance machine are defined underneath:

• Student:

o Generates a personalized QR code using the

Android app, which incorporates their specific

identification details which include roll wide variety

and student ID.

o Presents the QR code for scanning throughout

attendance recording.

• Teacher/Administrator:

o Uses the Android app to scan student QR codes

during class or events.

o Manages attendance records for specific subjects

or sessions.

o Generates and reviews attendance sheets

automatically created by the system.

QR Code-Based Attendance System Using Deep Learning

385

• System:

o Validates scanned QR codes to ensure accuracy

and prevent duplication.

o Stores attendance data securely in a database.

o Generates real-time attendance reports for

teachers or administrators.

This use case (figure 1) highlights the seamless

interaction between students, teachers, and the system,

ensuring an efficient and reliable attendance tracking

process.

Figure 1: Use Case Diagram.

4.6 Data Flow Diagram (DFD)

The data flow diagram (DFD) of the system is

illustrated in Figure 2:

Figure 2: DFD.

4.7 Database

The scholar attendance database is managed by the

use of SQLite. It shops the subsequent facts:

• Subject name and code.

• Student name and ID.

• Attendance repute (present/absent).

The database desk structure for a specific issue is

shown underneath in figure 3:

Figure 3: The Database Table Schema for Individual

Subject Records.

4.8 Sequence Diagram

The system's operational workflow is depicted in the

sequence diagram (figure 4):

Figure 4: Sequence Diagram.

The sequence diagram demonstrates the step-by-step

interaction between the student, professor, and the

system, including:

• Student QR code generation and

presentation.

• The professor scanned the QR code and

recorded attendance.

• System validation and storage of attendance

data.

• Generation of attendance reports.

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

COMMUNICATION, AND COMPUTING TECHNOLOGIES

386

4.9 Flowchart

The flowchart of the QR Code-Based Attendance

System is shown in figure 5:

Figure 5: Flowchart of the Application System.

5 EXECUTION AND OUTCOMES

5.1 Model Evaluation

We tested a range of deep learning architectures:

• Model-1: CNN (Our proposed framework for

capturing temporal characteristics).

• Model-2: LSTM (Captures long-range

dependencies in price fluctuations).

• Model-3: GRU (Gated Recurrent Units for

efficient sequence analysis).

• Model-4: Random Forest (Baseline machine

learning approach for reference).

• Model-5: ARIA (Classic model for time-series

forecasting).

5.2 Export the Attendance to

CSV/EXCLS Files

• To export the overall attendance sheet in CSV

or XLS format, the trainer or administrator need

to pick out the relevant problem code. The

device then compiles the attendance records and

generates a file in which:

• 0 represents an absent status.

• 1 represents a present status.

Below is a screenshot (figure 6) of the generated CSV

file, showcasing the attendance records in a structured

and easily accessible format.

This feature allows for efficient record-keeping and

analysis of attendance data, making it convenient for

teachers and administrators to manage and review

attendance trends.

Figure 6: Export the Attendance to CSV/EXCLS Files.

6 CONCLUSIONS

The advanced gadget offered in this paper has been

efficiently designed and examined, demonstrating its

functionality to analyze and export pupil attendance

reputation correctly. Attendance tracking structures

play a crucial position in our daily lives, and among

numerous code-scanning technologies, the QR Code-

Based Attendance System will be the most correct

and reliable. In this task document, we have delivered

the idea of the Attendance Monitoring System and

highlighted its numerous advantages. By leveraging

QR code generation, this machine provides an

efficient and green answer for storing attendance

records digitally on smartphones, doing away with

the want for paper-based strategies. This not only

enhances accuracy however additionally contributes

to sustainability and comfort, making it an ideal

desire for modern-day educational and professional

environments.

7 FUTURE EXTENT

Our next paintings will focus on giving pupils access

to notes and ignored elegant issues. Complete control

over instructors with safer and more reliable

substitutes. Lastly, we conclude that this attendance

tracking system will solve the problem of actual

worldwide attendance if it is combined with a facial

recognition device.

QR Code-Based Attendance System Using Deep Learning

387

REFERENCES

Brownlee, J. (2020). "Deep Learning for Time Series

Forecasting: Predict the Future with MLPs, CNNs, and

LSTMs in Python." Machine Learning Mastery.

Chaitanya, V. Lakshmi. "Machine Learning Based Predicti

ve Model for Data Fusion Based Intruder Alert

System." journal of algebraic statistics 13.2 (2022):

2477-2483

Devi, M. Sharmila, et al. "Extracting and Analyzing

Features in Natural Language Processing for Deep

Learning with English Language." Journal of Research

Publication and Reviews 4.4 (2023): 497-502.

FAO. (2021). "The State of Agricultural Commodity Mark

ets: Trends and Challenges in Price Volatility." FAO

Publications.

Food and Agriculture Organization (FAO). (2023). "Global

Information and Early Warning System (GIEWS) on

Food and Agriculture." FAO Publications.

Li, H., et al. (2021). "A Comparative Analysis of Machine

Learning and Deep Learning Techniques for Time-

Series Forecasting in Agricultural Markets." IEEE

Transactions on AI, 29(4), 320-333.

Mishra, D., & Singh, P. (2020). "A Survey of Machine

Learning Methods for Predicting Crop Prices." Interna

tional Journal of Data Science, 12(2), 112-125.

Mr.M.Amareswara Kumar, “Baby care warning system

based on IoT and GSM to prevent leaving a child in a

parked car” in International Conference on Emerging

Trends in Electronics and Communication Engineering

- 2023, API Proceedings July-2024

Parumanchala Bhaskar, et al. "Machine Learning Based

Predictive Model for Closed Loop Air Filtering

System." Journal of Algebraic Statistics 13.3 (2022):

416-423.

Parumanchala Bhaskar, et al. "Incorporating Deep Learning

Techniques to Estimate the Damage of Cars During the

Accidents" AIP Conference Proceedings. Vol. 3028.

No. 1. AIP Publishing, 2024.

Patel, A., & Kumar, R. (2022). "Utilizing Deep Learning

for Predicting Agricultural Prices." Journal of AI

Research in Agriculture, 18(3), 45-57.

Sharma, R., & Gupta, K. (2023). "Agricultural Market

Forecasting Using Hybrid CNN- LSTM Models." Co

mputational Intelligence in Agriculture, 30(4), 201-

218.

World Bank. (2022). "Commodity Markets Outlook: The

Impact of Climate Change on Agricultural Prices."

World Bank Group.

Zhang, X., et al. (2019). "Models for Price Prediction in

Agricultural Markets Using Neural Networks." Applied

AI Review, 27(1), 98-115.

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

COMMUNICATION, AND COMPUTING TECHNOLOGIES

388