International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 07 | July 2024
www.irjet.net
p-ISSN: 2395-0072
Facial Recognition-Based Attendance System Using Local Binary Pattern Histogram Algorithm: A Comprehensive Approach Aarya Sutar1, Parth Shah2, Yashika Sonchatra3, Priyanka Deshmukh4 1Graduate Student, K.J. Somaiya Institute of Technology, Mumbai, Maharashtra, India 2Graduate Student, K.J. Somaiya Institute of Technology, Mumbai, Maharashtra, India 3Graduate Student, K.J. Somaiya Institute of Technology, Mumbai, Maharashtra, India
4Assistant Professor (Computer Department), K.J. Somaiya Institute of Technology, Mumbai, Maharashtra, India
---------------------------------------------------------------------***--------------------------------------------------------------------2. BACKGROUND
Abstract -In this digital era, face recognition system plays
a vital role in almost every sector. Face recognition is one of the mostly used biometrics. It can used for security, authentication, identification, and has got many more advantages. Despite having low accuracy when compared to iris recognition and fingerprint recognition, it is being widely used due to its contactless and non-invasive process in this project, we have developed a platform for an attendance system. we have tried to eliminate the traditional system of attendance by using the main feature of a user i.e., his/her face. So, our system basically uses faces to mark the attendance for a particular session. This system consists of four phases- database creation, face detection, face recognition, attendance updating. Database is created by the images of the students in class. Face detection and recognition is performed using OpenCV and Local Binary Pattern Histogram algorithm respectively. Faces are detected and recognized from live streaming video of the classroom.
Face recognition is an important technology used to recognize people in all areas of daily life and is closely related to the perception of the human brain. The human retina interprets light patterns, classifies shapes, sizes and textures, and compares them to stored symbols to recognize faces. This process is difficult to replicate in technology, but the large memory and processing power of computers help overcome human limitations. These features make face recognition a good biometric method by comparing real-time images with images in the database to ensure accuracy. In airport security, it increases security by identifying passengers according to the watch list. It helps police track and arrest suspects in criminal investigations. Social media platforms use facial recognition to tag people in photos so they can be easily identified and contacted. Facial recognition can also be used to lock and unlock personal devices, providing a secure and convenient way to protect user data. Important points have been made along the way. A key first step was the introduction of principal component analysis (PCA) in 1988. Over the years, further advances in machine learning and artificial intelligence have increased the accuracy and speed of facial recognition machines, making them reliable and widely applicable. Deep learning and advanced algorithms to deal with various challenges such as changes in lighting, faces, and orientation. Its integration with security systems, social media, and personal devices demonstrates its importance in daily life, providing both convenience and enhanced security.
Key Words: Face Recognition, Biometrics, Security, Authentication, Attendance System, OpenCV, Local Binary Pattern Histogram
1.INTRODUCTION In today’s colleges, attendance management is critical to academic success, but traditional methods are inefficient and error-prone. Our advanced facial attendance technology uses facial recognition to track attendance accurately and efficiently without the need for in-person or written checks. The system instantly captures and processes student facial images to ensure accurate data does not disrupt the flow of the classroom. By eliminating manual input, teachers can focus more on teaching. Facial recognition technology provides high accuracy across multiple situations, reducing errors made with manual methods. Detailed reporting will help identify regular attendance and monitor overall status to provide timely support to students. Our face-based attendance system improves learning outcomes by simplifying attendance management and providing insight. Detailed reports help identify irregular attendance and track overall trends, enabling prompt support for students.
© 2024, IRJET
|
Impact Factor value: 8.226
3. PROBLEM STATEMENT The process of student attendance, such as attendance or passing an attendance sheet, can interfere with teaching and examination. This process not only takes up valuable classroom time, but also adds repetitive work for teachers to count students and keep attendance records. Students are also prone to cheating as their friends can register to not come to school. To address these issues, facial recognition systems offer attendance tracking solutions
|
ISO 9001:2008 Certified Journal
|
Page 1310