Authors: TN Krishnan Embranthiri, Maxin Shajan , Joepaul Jose , Prof. Anila S
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Attendance management is a crucial task in various domains, such as educational institutions, workplaces, and events, to accurately record and monitor the presence of individuals. Traditional attendance marking methods involving manual processes are time-consuming, prone to errors, and lack efficiency. This abstract proposes a novel solution, the \"Attendance Marker Using Facial Recognition,\" which leverages the advancements in facial recognition technology to automate attendance tracking The attendance marker system offers numerous advantages over traditional methods. Firstly, it eliminates the need for manual data entry, reducing human error and increasing efficiency. It also ensures reliable identification, as facial features are difficult to forge or replicate. Moreover, the system can handle a large number of attendees simultaneously, making it suitable for events with high footfall
Maintenance of an attendance system is crucial for monitoring student performance across all institutes in this system. Teachers use attendance sheets in the majority of the institution's traditional attendance marking system. Students will either sign the attendance form and file it or log on to the computer for later review. This method is known as tiresome. As some of the students frequently sign for their pals, this is time-consuming and inaccurate. Keeping track of every student's attendance in a big classroom is challenging. It is tiresome to keep track of students' attendance in class. The system for recording attendance includes facial recognition, stream image processing, and storage of the data in a database that is maintained by the teachers. creates a database of the faculty, staff, and students. An automated attendance marker is a system that is used to track the attendance of individuals in an organization, such as a school or business. It typically works by using some form of identification, such as a face, fingerprint, or RFID card, to verify the identity of the individual and mark their attendance in a database or on a physical attendance sheet. Automated attendance markers can provide a convenient and efficient way to track attendance, as they can save time and reduce the risk of errors compared to manual attendance tracking methods. They may also be used for a variety of purposes, such as calculating pay for hourly employees or tracking attendance for course credit.
Types: There are several types of automated attendance markers, which use different methods to identify individuals and mark their attendance. Some common types include:
Face recognition systems, which use cameras and computer vision algorithms to identify individuals based on their facial features.Fingerprint scanners, which use fingerprint scanners to identify individuals based on the unique patterns of their fingerprints.RFID (Radio-Frequency Identification) systems, which use RFID cards or tags to identify individuals when they are within range of a reader.
Advantages: Automated attendance markers can provide a number of advantages over manual attendance tracking methods. For example:They can save time and effort, as individuals do not need to sign in or out manually.They can reduce the risk of errors, such as misinterpreting handwritten names or accidentally marking the wrong person as present.They can provide more accurate and up-to-date attendance records, which can be useful for a variety of purposes (e.g., tracking attendance for course credit, calculating pay for hourly employees). They can be more convenient for individuals, who do not need to remember to bring an attendance sheet or sign in manually
II. RELATED WORKS
A. Paper 1: Recognition based Attendance System for Classroom Environment
The system then extracts features from the images using a facial recognition algorithm and compares them to the enrolled students' images in the database. If a match is found, the system can mark the student's attendance in the database or on a physical attendance sheet.
3. Accuracy: The accuracy of the attendance marker will depend on a variety of factors, including the quality of the images captured, the size of the enrolled population, and the similarity of the enrolled students. In general, systems that use facial recognition tend to be more accurate when the enrolled population is small and the students are relatively distinct from one another.
B. Paper 2: Automatic Attendance System Using Face Recognition By Viola Jones Algorithm
C. Paper 3: Class Room Attendance System Using A3D Facial Model
D. Paper 4 Attendance Marking Using RFID
III. PROPOSED METHODOLOGY
The system proposed in the basis of face recognition. When a student come across the camera module, then his/her image/photo will be captured and recognize with validation. When recognition and validation is succeeded, then his/her attendance will mark automatically. In this system, user gets a login interface to interact with the system.
, interface displays the home page of the proposed system. The proposed block diagram of the automatic attendance system is shown in the Fig
IV. TECHNOLOGIES USED
The Local Binary Pattern (LBP) algorithm is a method used for texture classification in computer vision. It works by comparing each pixel in an image to its neighbors, and creating a binary code based on whether each neighbor is greater than or less than the pixel value. The resulting codes for all pixels in an image are then used to create a histogram, which can be used to represent the texture of the image.
The LBP algorithm has a number of variations, such as the Multiresolution Gray-Scale and Rotation Invariant Texture Classification (MG-RI-LBP) and the Spatial Gray-Scale and Rotation Invariant Texture Classification (SG-RI-LBP). These variations aim to improve the robustness and accuracy of the LBP algorithm.
In practice, the LBP algorithm is often used in combination with other techniques, such as support vector machines (SVMs), to classify images based on their texture. It has been applied in a wide range of applications, including face recognition, fingerprint identification, and terrain classification.
GUI will be Web application
a. Face model Training
2. Admin module
a. Login with username and password
b. Register Student with basic details (username is the name given at the time of face model training) and also adding parent details with email
c. View Student details
d. View attendance details
e. Add Subjects and time for attendance
3. Student module
a. View Attendance
b. Mark Attendance
This admin panel contains:
VII. FUTURE ENHANCEMENTS
Integration with Student Information Systems: Integrate the automated attendance marker system with existing student information systems or learning management systems, enabling seamless data synchronization and generating comprehensive attendance reports and analytics.
Privacy and Security: Implement robust privacy and security measures to protect the collected biometric data and ensure compliance with data protection regulations. This can involve encryption, secure storage, and access control mechanisms.
Real-World Testing and Validation: Conduct extensive real-world testing and validation of the system in different environments, including classrooms, lecture halls, and large-scale events, to assess its performance and robustness in various scenarios.
Continuous System Improvement: Continuously monitor and evaluate the system's performance, gather feedback from users, and implement iterative improvements to enhance accuracy, reliability, and usability based on practical experiences and evolving technology advancements.
We are very thankful to the Department of Computer Science and Engineering of Adi Shankara Institute of Engineering and Technology for permitting us to work on the topic “Automated attendance marker using facial recognition”. We truly express our gratitude to Prof. ANILA S, Department of CSE, ASIET for giving constant support and guidance.
In conclusion, the automated attendance marker system that utilizes the LBPH (Local Binary Patterns Histograms) algorithm offers an efficient and reliable approach for managing attendance based on face recognition. By following a series of steps including data collection, preprocessing, feature extraction, training, recognition, and matching, the system can accurately identify individuals and mark their attendance automatically. The LBPH algorithm extracts local texture patterns from preprocessed facial images, converting them into binary codes that form feature vectors for each face. These feature vectors are used for training the algorithm, enabling it to learn and differentiate between different individuals. During recognition, the system captures face images, preprocesses them, and extracts feature vectors for comparison with stored face templates. By matching the feature vectors and applying a confidence threshold, the system can accurately identify enrolled students and mark their attendance accordingly. The utilization of the LBPH algorithm in the automated attendance marker system provides several advantages, including its robustness to variations in lighting conditions, facial expressions, and angles. Additionally, the algorithm\'s simplicity and efficiency make it suitable for real-time attendance tracking applications. Overall, the automated attendance marker system based on the LBPH algorithm enhances efficiency, accuracy, and convenience in managing attendance processes, minimizing manual effort, and ensuring reliable attendance records.
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Copyright © 2023 TN Krishnan Embranthiri, Maxin Shajan , Joepaul Jose , Prof. Anila S. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.