Traditional attendance methods like manual entry and RFID-based systems are slow, errorprone, and

vulnerable to manipulation, creating the need for a more secure and efficient solution. To address these issues,

an AI-powered Automated Attendance Management System (AAMS) is proposed, integrating computer vision

and machine learning techniques for realtime face detection and recognition. Developed using Python, the

system leverages OpenCV for image preprocessing, while SQLite and MySQL are used for secure data storage

and management. The core methodology involves three stages: face detection, feature extraction, and identity

to revolutionize attendance tracking while enhancing operational efficiency and security.

This research introduces an automated attendance system that uses advanced deep learning techniques for real-

time face recognition, linked with a secure database to record attendance data. The goal is to cut down on

manual work, stop fake attendance, and offer a system that is easy to use, scalable, and hygienic, suitable for

both schools and companies. Manual methods for tracking attendance are becoming harder to rely on because

they are slow, can be easily cheated, and involve physical contact, which can be risky.

The COVID-19 pandemic has made it clear how important it is to have contactless, automatic solutions that

keep people safe while still being accurate and dependable. Also, as schools and workplaces grow bigger and

Recent studies on automated attendance systems have aimed to improve upon the weaknesses of older manual

and semi-automated methods by using face recognition technology. Jadhav et al. [1] created a system that uses

OpenCV along with Haar cascade for face detection and LBPH for recognition, which helps fix the problems

with manual methods that are slow, error-prone, and can be easily faked. Building on this, Mahboob and Qadir

[2] pointed out that traditional methods and early face recognition systems still have issues with errors and fake

attendance, especially when people wear masks or have tattoos, which can affect accuracy. Other researchers

have looked into improving the recognition algorithms themselves.

Raj et al. [3] noted that methods like Eigenfaces and Fisher faces are very sensitive to changes in lighting, and

showed that LBPH is a better option because it's more reliable. Similarly, Abraham et al. [4] found that

differences in facial features, such as glasses or beards, can reduce recognition accuracy, which shows the need

solutions, some researchers have suggested combining different technologies to increase reliability.

Sawhney et al. [7] introduced a real-time smart attendance system designed to cut down on fake or proxy

attendance in schools and workplaces. Akbar et al. [8] merged face recognition with RFID technology to tackle

issues with cost, integration, and data storage that were present in earlier systems using Raspberry Pi or

ATmega32 microcontrollers.

Overall, these studies show that face recognition is becoming more popular for managing attendance, with

steady improvements in accuracy, speed, and ease of use.

However, there are still challenges to overcome, such as dealing with partial face coverings like masks or

scarves, making the system work well in real-world settings, handling large numbers of users efficiently, and

ensuring that the system can process data quickly enough for real-time use.

This research paper introduces a detailed method for an automated attendance system that uses computer vision

and machine learning to make attendance tracking more accurate and efficient in real time. The main idea is to

use face recognition technology along with a webcam and a database system to automatically track attendance

in classrooms or work environments. This system removes the problems of manual methods by taking facial

features, training recognition models, and marking attendance as it happens. The main idea of this work is that

The system uses a custom dataset created during the registration process.

The dataset is adaptable and expands as more students register, making it suitable for use in bigger classrooms

or organizations.

Experimental Setup

To thoroughly check how well the proposed Face Recognition-Based Attendance Management System works,

a series of experiments were carefully planned and carried out.

Analysis

O = {A}

O = Update (I

S = {{I

db

The performance of a facial recognition attendance system heavily depends on the quality of datasets. Limited

or imbalanced datasets lacking diversity in lighting, pose, and facial occlusions lead to overfitting and poor

generalization in real-world scenarios. Mislabeled or insufficient data can cause inaccurate recognition and

adjustments can improve robustness, but collecting large, well-annotated datasets remains a major challenge.

Camera quality and placement significantly impact system accuracy. Low-resolution or lowlight cameras

reduce the reliability of face detection and recognition, especially in crowded or poorly illuminated

environments. Inconsistent angles or moving cameras introduce variability that complicates real-time

used, securing sensitive biometric data through encryption and role-based access is essential to protect privacy

and prevent unauthorized use. Real-world deployments face several issues, including privacy concerns,

environmental variability, and hardware limitations. Sudden changes in lighting, occlusions like masks, and

similar-looking individuals can lower accuracy. Additionally, biometric data is highly sensitive, requiring strict

compliance with data protection laws. Implementing secure data transfer, access logging, and fallback

authentication mechanisms, along with regular system monitoring, is crucial to maintain reliability and trust.

The proposed AI-powered Automated Attendance Management System (AAMS) successfully addresses the

limitations of traditional attendance methods by integrating computer vision and machine learning techniques.

By utilizing Haar Cascade Classifier for face detection and LBPH algorithm for face recognition, the system