The document describes a proposed face recognition system to automate student attendance tracking in educational institutions. The system would use a camera to capture student faces and compare them to a database of registered student photos using deep learning algorithms. If a match is found, the student's attendance would be logged electronically. The system aims to make the attendance process faster and more accurate than traditional paper-based methods. It provides various technical details on how the face detection, recognition and matching would work using tools like Haar classifiers, neural networks and OpenCV. Advantages of the automated approach are also discussed.
This document discusses the development of an automated attendance system using face recognition technology. It begins by outlining the challenges of traditional manual attendance tracking methods. The proposed system would use cameras and face detection/recognition software to identify and mark students as present. It then reviews related work on face recognition and attendance systems using RFID cards or biometrics. The objectives are defined as developing a real-time face detection and recognition system to automate attendance tracking. Finally, it discusses potential methods for face detection, recognition algorithms, and using sensors and microcontrollers like Arduino for proof-of-concept prototyping.
Face Recognition based Smart Attendance System Using IoTIRJET Journal
This document describes a face recognition-based smart attendance system using IoT. The system uses a Raspberry Pi connected to a webcam to take pictures of students' faces as they enter the classroom. It then applies face detection and recognition techniques to identify the students and mark them as present in an Excel attendance sheet along with their details. The system aims to automate attendance taking and eliminate issues like proxy attendance. It stores student data and images to create a dataset, which it then uses for real-time face recognition and attendance marking as students' faces are detected by the webcam. The results show this system can accurately and efficiently automate attendance taking in a contactless manner.
Attendance System using Face RecognitionIRJET Journal
This document describes a proposed attendance system that uses face recognition technology. It begins with an introduction to traditional attendance methods and their limitations. It then discusses the proposed system, which would use face detection and recognition algorithms to automatically mark student attendance from webcam images. Specifically, it would use the Haar cascade algorithm for face detection and KNN (k-nearest neighbors) for face recognition. The document outlines the system design, including an enrollment process to store student face data and an attendance marking process to recognize students in real-time. It suggests this system could automate attendance in a more secure, reliable and time-efficient way compared to traditional methods.
IRJET- Autonamy of Attendence using Face RecognitionIRJET Journal
This document summarizes an automated attendance system using video-based face recognition. The system works by capturing a video of students in a classroom and using face detection and recognition algorithms to identify and mark the attendance of each student. It first detects faces in each video frame using the Haar cascade classifier, then recognizes the faces by comparing them to a training database of student faces using the Eigenfaces algorithm. Finally, it registers the attendance in an Excel sheet. The system aims to make the attendance process more efficient and accurate compared to traditional manual methods.
Attendance management system using face recognitionIAESIJAI
Traditional attendance systems consist of registers marked by teachers, leading to human error and a lot of maintenance. Time consumption is a key point in this system. We wanted to revolutionize the digital tools available in today's time i.e., facial recognition. This project has revolutionized to overcome the problems of the traditional system. Face recognition and marking the present is our project. A database of all students in the class is kept in single folder, and attendance is marked if each student's face matches with one of the stored faces. Otherwise, the face is ignored and not marked for attendance. In our project, face detection (machine learning) is used.
Implementation of Automatic Attendance Management System Using Harcascade and...IRJET Journal
This document proposes an automatic attendance management system using facial recognition algorithms. It aims to reduce human error and resources required for manual attendance recording. The system uses a camera to capture faces at the entrance and matches them to employee photos stored in a database using Haar cascade detection and local binary pattern recognition. If a match is found, the employee is marked present and their attendance updated in real time to an Excel sheet for administrators to view. The system is intended to help organizations more efficiently track attendance compared to traditional paper-based methods.
Attendance System using Face RecognitionIRJET Journal
This document describes an automated attendance system using face recognition. It discusses using algorithms like Viola-Jones for face detection and PCA for feature extraction and SVM for classification. The system works by capturing images of students' faces with a camera as they enter the classroom. It then detects faces, recognizes the students, and automatically marks their attendance on an attendance sheet. The system is presented as an improvement over previous biometric-based attendance systems in that it is faster, more convenient, and helps monitor students.
IRJET- Intelligent Automated Attendance System based on Facial RecognitionIRJET Journal
This document presents a proposed intelligent automated attendance system based on facial recognition. The system aims to automate the attendance marking process in educational institutions to make it faster and less error-prone compared to manual methods. It works by using computer vision techniques like haar cascade classification for face detection and local binary pattern histograms for face recognition. The system architecture involves capturing images, detecting faces, recognizing students by matching faces to a training database, and marking the attendance automatically. Algorithms like haar cascade and local binary patterns are used for face detection and recognition. The proposed system aims to solve issues with existing manual and automated attendance systems like time consumption, errors, and lack of accuracy.
This document discusses the development of an automated attendance system using face recognition technology. It begins by outlining the challenges of traditional manual attendance tracking methods. The proposed system would use cameras and face detection/recognition software to identify and mark students as present. It then reviews related work on face recognition and attendance systems using RFID cards or biometrics. The objectives are defined as developing a real-time face detection and recognition system to automate attendance tracking. Finally, it discusses potential methods for face detection, recognition algorithms, and using sensors and microcontrollers like Arduino for proof-of-concept prototyping.
Face Recognition based Smart Attendance System Using IoTIRJET Journal
This document describes a face recognition-based smart attendance system using IoT. The system uses a Raspberry Pi connected to a webcam to take pictures of students' faces as they enter the classroom. It then applies face detection and recognition techniques to identify the students and mark them as present in an Excel attendance sheet along with their details. The system aims to automate attendance taking and eliminate issues like proxy attendance. It stores student data and images to create a dataset, which it then uses for real-time face recognition and attendance marking as students' faces are detected by the webcam. The results show this system can accurately and efficiently automate attendance taking in a contactless manner.
Attendance System using Face RecognitionIRJET Journal
This document describes a proposed attendance system that uses face recognition technology. It begins with an introduction to traditional attendance methods and their limitations. It then discusses the proposed system, which would use face detection and recognition algorithms to automatically mark student attendance from webcam images. Specifically, it would use the Haar cascade algorithm for face detection and KNN (k-nearest neighbors) for face recognition. The document outlines the system design, including an enrollment process to store student face data and an attendance marking process to recognize students in real-time. It suggests this system could automate attendance in a more secure, reliable and time-efficient way compared to traditional methods.
IRJET- Autonamy of Attendence using Face RecognitionIRJET Journal
This document summarizes an automated attendance system using video-based face recognition. The system works by capturing a video of students in a classroom and using face detection and recognition algorithms to identify and mark the attendance of each student. It first detects faces in each video frame using the Haar cascade classifier, then recognizes the faces by comparing them to a training database of student faces using the Eigenfaces algorithm. Finally, it registers the attendance in an Excel sheet. The system aims to make the attendance process more efficient and accurate compared to traditional manual methods.
Attendance management system using face recognitionIAESIJAI
Traditional attendance systems consist of registers marked by teachers, leading to human error and a lot of maintenance. Time consumption is a key point in this system. We wanted to revolutionize the digital tools available in today's time i.e., facial recognition. This project has revolutionized to overcome the problems of the traditional system. Face recognition and marking the present is our project. A database of all students in the class is kept in single folder, and attendance is marked if each student's face matches with one of the stored faces. Otherwise, the face is ignored and not marked for attendance. In our project, face detection (machine learning) is used.
Implementation of Automatic Attendance Management System Using Harcascade and...IRJET Journal
This document proposes an automatic attendance management system using facial recognition algorithms. It aims to reduce human error and resources required for manual attendance recording. The system uses a camera to capture faces at the entrance and matches them to employee photos stored in a database using Haar cascade detection and local binary pattern recognition. If a match is found, the employee is marked present and their attendance updated in real time to an Excel sheet for administrators to view. The system is intended to help organizations more efficiently track attendance compared to traditional paper-based methods.
Attendance System using Face RecognitionIRJET Journal
This document describes an automated attendance system using face recognition. It discusses using algorithms like Viola-Jones for face detection and PCA for feature extraction and SVM for classification. The system works by capturing images of students' faces with a camera as they enter the classroom. It then detects faces, recognizes the students, and automatically marks their attendance on an attendance sheet. The system is presented as an improvement over previous biometric-based attendance systems in that it is faster, more convenient, and helps monitor students.
IRJET- Intelligent Automated Attendance System based on Facial RecognitionIRJET Journal
This document presents a proposed intelligent automated attendance system based on facial recognition. The system aims to automate the attendance marking process in educational institutions to make it faster and less error-prone compared to manual methods. It works by using computer vision techniques like haar cascade classification for face detection and local binary pattern histograms for face recognition. The system architecture involves capturing images, detecting faces, recognizing students by matching faces to a training database, and marking the attendance automatically. Algorithms like haar cascade and local binary patterns are used for face detection and recognition. The proposed system aims to solve issues with existing manual and automated attendance systems like time consumption, errors, and lack of accuracy.
IRJET- Implementation of Attendance System using Face RecognitionIRJET Journal
This document describes a study that implemented an attendance tracking system using face recognition. The system aims to automatically record students' attendance during lectures using facial recognition technology instead of manual methods. It discusses existing manual and computer-based attendance systems and proposes a system that uses PCA (Principal Component Analysis) face recognition techniques to detect and recognize students' faces from images captured during lectures in order to mark their attendance automatically. The system architecture involves enrolling students by taking their images and extracting features, then acquiring new images during lectures, enhancing them, detecting and recognizing faces to mark attendance on a server database. The study implemented this system using Visual Studio 2010 and MS SQL Server 2008 and found it could successfully recognize faces and record attendance.
IRJET-Human Face Detection and Identification using Deep Metric LearningIRJET Journal
This document discusses a project that uses deep metric learning techniques for human face detection and identification in images and videos. Deep metric learning outputs a real-valued vector rather than a single classification. It uses libraries like OpenCV, Dlib, scikit-learn and Keras to build neural networks for facial recognition. The goals are to develop a system that can identify faces even from low quality images with variations in illumination, expression, angle and occlusions. Existing face recognition has challenges in these conditions, so the aim is to improve accuracy rates for normal and non-ideal images through deep metric learning approaches.
IRJET- Free & Generic Facial Attendance System using AndroidIRJET Journal
This document proposes a free and generic facial attendance system using Android that can automatically detect students' faces and mark attendance. It uses face detection and recognition algorithms to capture images from a camera and identify students by matching faces to a database. If a face is detected, attendance is marked as present. The system then creates a Google Sheet to store and access attendance records. This provides a low-cost alternative to commercial biometric systems for tracking student attendance.
This document describes a face recognition attendance system. The system uses face recognition techniques to automatically take attendance by detecting and identifying students' faces from live classroom video streams. It aims to address issues with traditional manual attendance methods, which are tedious and prone to errors. The system works in four stages: data collection, face detection, face preprocessing, and face recognition & attendance updating. Faces are detected using Haar Cascade classifiers and further processed using Local Binary Pattern histograms for recognition. When a known face is identified, the student's attendance is automatically marked. The system is designed to provide a more efficient alternative to manual attendance marking.
This document describes a face recognition attendance system that was designed to automate the manual attendance marking process in colleges and universities. The system uses face recognition techniques including face detection, preprocessing, feature extraction, and recognition to identify students from images captured in the classroom and automatically mark their attendance. It discusses related works on biometric attendance systems using technologies like iris recognition and fingerprints. The system design incorporates a teacher module, student module, and functionality for processing images, extracting features, classifying faces, and updating attendance records. It evaluates the methodology used for face recognition, preprocessing, and non-real time recognition and concludes the automated system helps improve accuracy and speed compared to manual attendance marking.
Comparative Analysis of Face Recognition Methodologies and TechniquesFarwa Ansari
In the field of computer sciences such as
graphics and also analyzing the image and its processing,
face recognition is the most prominent problem due to the
comprehensive variation of faces and the complexity of
noises and image backgrounds. The purpose and working
of this system is that it identifies the face of a person from
the real time video and verifies the person from the images
store in the database. This paper provides a review of the
methodologies and techniques used for face detection and
recognition. Firstly a brief introduction of Facial
Recognition is given then the review of the face
recognition’s working which has been done until now, is
briefly introduced. Then the next sections covered the
approaches, methodologies, techniques and their
comparison. Holistic, Feature based and Hybrid
approaches are basically used for face recognition
methodologies. Eigen Faces, Fisher Faces and LBP
methodologies were introduced for recognition purpose.
Eigen Faces is most frequently used because of its
efficiencies. To observe the efficient techniques of facial
recognition, there are many scenarios to measure its
performance which are based on real time.
Automated attendance system using Face recognitionIRJET Journal
This document describes an automated attendance system using face recognition. The system uses image capture to take photos of students entering the classroom. It then uses the Viola-Jones algorithm for face detection and PCA for feature selection and SVM for classification to recognize students' faces and mark their attendance automatically. When compared to traditional attendance methods, this system saves time and helps monitor students. It discusses related work using RFID, fingerprints, and iris recognition for attendance systems. It outlines the proposed system's modules for image capture, face detection, preprocessing, database development, and postprocessing. Finally, it discusses results, conclusions, and opportunities for future work to improve recognition rates under various conditions.
In today’s world student attendance system in any institution is a lengthy process and consuming the time. Usually we know that RFID card identify the student based on the card. But it allows the bogus attendance .This paper based to approach the compact and reliable classroom attendance system by using RFID card and face recognition technique. Here using MATLAB progress to verify the face of the each student exclusively. Then an individual buzzer is used in this paper. It is identifying the proxy attendance and stored in a log file. Then this paper notices the absentees list and sent the message to respective or predefined number. Here RFID and face verified to use and take attendance system that efficiency is 98%.
1. The document discusses using facial recognition technology for ATM security to prevent unauthorized access through stolen cards or PINs. It analyzes existing facial recognition methods like eigenfaces and proposes using 3D recognition to address spoofing issues.
2. The methodology section outlines the steps - locating an open source facial recognition program using local feature analysis, extracting features from faces, and searching databases to find matches.
3. Results show that Bank United was the first to use iris recognition at ATMs for a cardless, password-free way to withdraw money. The conclusion is that facial recognition is highly secure and widely used in security applications due to technological advances in identification and verification.
Development of an Automatic & Manual Class Attendance System using Haar Casca...IRJET Journal
This document presents a proposed system for an automatic and manual class attendance system using facial recognition. The system uses Haar cascade classifiers for facial detection and recognition. A camera would be installed at the entrance of a classroom to capture images of students' faces as they enter. Using local binary patterns histograms (LBPH) algorithm, the captured faces would be matched to images stored in a database to automatically record attendance. For students not registered in the database, a manual attendance process would allow attendance to be marked by providing enrollment ID and name. The proposed system aims to digitize and streamline traditional paper-based attendance systems while addressing issues like proxy attendance.
This document describes a human face recognition attendance system created by two students. It aims to build a system that can recognize faces and match them to a database to take attendance, making the process more secure and accurate than traditional methods. The system will use a camera to capture faces, and compare them using OpenCV on a Raspberry Pi running Raspbian to match faces in the database. It outlines the plan to divide the work into face detection/capture and matching, and provides timelines and budgets for the project. The motivation is to automate the attendance process and increase security by preventing others from signing in falsely.
Facial recognition systems use computer applications to identify or verify people from images or video by comparing facial features to a database. They analyze over 80 nodal points on faces, such as eye distance and nose width. 3D modeling provides more accuracy by measuring curves and creating unique templates to match against databases. While useful for security and IDs, facial recognition raises privacy issues if misused due to its ability to identify people without consent.
Self-X: Geo Fencing and Face Recognition based Smart Attendance Management Ap...IRJET Journal
The document describes a proposed smart attendance management application called Self-X that uses geo-fencing and face recognition technologies. It aims to develop a flexible mobile-based attendance system that can optimize and accelerate the attendance process, saving time and resources compared to traditional systems. The proposed system uses geo-fencing to authenticate student locations and a face recognition model to identify students from photos in order to automatically mark attendances. It is intended to address issues like fraudulent attendance faced by previous biometric and barcode-based systems.
Real Time Image Based Attendance System using PythonIRJET Journal
The document describes a proposed real-time image-based attendance system using facial recognition in Python. It involves four main steps: 1) capturing images using a webcam, 2) preprocessing the images by converting them to grayscale, 3) applying facial recognition algorithms like Haar Cascade and LBPH to detect and recognize faces, and 4) storing the attendance data in a database like CSV files. Previous related works that implemented similar systems using techniques like OpenCV, Viola-Jones, and deep learning algorithms are also discussed. The proposed system aims to provide an accurate, efficient and user-friendly alternative to traditional paper-based attendance methods.
Face Recognition Based Automated Student Attendance Systemijtsrd
Face recognition system is very beneficial in real time applications, concentrated in security control systems. Face Detection and Recognition is a vital area in the province of validation. In this project, the Open CV based face recognition strategy has been proposed. This model integrates a camera that captures an input image, an algorithm Haar Cascade Algorithm for detecting face from an input image, identifying the face and marking the attendance in an excel sheet. The proposed system implements features such as detection of faces, extraction of the features, exposure of extracted features, analysis of students attendance, and monthly attendance report generation. Faces are recognized using advanced LBP using the database that contains images of students and is used to identify students using the captured image. Better precision is accomplished in results and the system takes into account the changes that occurs in the face over some time. Ms. Pranitha Prabhakar | Mr. Kathireshan "Face Recognition Based Automated Student Attendance System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38083.pdf Paper URL : https://www.ijtsrd.com/computer-science/other/38083/face-recognition-based-automated-student-attendance-system/ms-pranitha-prabhakar
A PROJECT REPORT ON IRIS RECOGNITION SYSTEM USING MATLABMaria Perkins
1. Iris recognition is a reliable biometric authentication method that uses the unique patterns in the iris to identify individuals.
2. Previous work has focused on detecting fake irises using techniques like analyzing image quality features, extracting texture features from the iris, and applying weighted local binary patterns.
3. Detecting fake irises using printed contact lenses is challenging but important for security. Methods have analyzed features like iris edge sharpness, iris-texton histograms, and gray-level co-occurrence matrices to differentiate real and fake irises.
4. Combining local descriptors like SIFT with local binary patterns can improve fake iris detection performance by making the approach
Effective Attendance Markingusing Face Recognition & RFID TagsIRJET Journal
This document proposes an effective attendance marking system using face recognition and RFID tags. The system aims to automate the attendance marking process. It works as follows:
1. A student holds their RFID tag close to a reader, which initiates the camera.
2. The student's face is then captured. Face recognition and matching algorithms based on MATLAB process and verify if the face matches the student's profile.
3. If a match is found, the student is marked as present. If not, they are marked absent. This process occurs for all students.
4. Students not present by a certain time limit will also be automatically marked absent. Parents can be messaged about absences.
Attendance System using Android Integrated Biometric Fingerprint RecognitionIRJET Journal
This document describes the development of an attendance tracking system using fingerprint recognition and an Android application. Key points:
- The system uses a fingerprint scanner to enroll students' fingerprints and store them in a database along with their IDs. When students place their finger on the scanner, it marks them as present by updating the database.
- An Android app was created to allow students and administrators to check attendance records in real-time by accessing the centralized database.
- The system aims to provide a cheaper and more reliable alternative to traditional paper-based attendance tracking while allowing remote attendance monitoring via the app.
- The system was tested on 5 students and 1 teacher, with fingerprint matching being about 100% accurate though some
This document describes a door lock system using face recognition for attendance marking. The proposed system uses a camera, processor and machine learning to identify authorized individuals and unlock the door. It aims to automate attendance marking by capturing facial images and recognizing students, while sending unauthorized access alerts. The system is designed to be low-cost, accurate and provide real-time monitoring using techniques like pre-processing, feature extraction and classification with Local Binary Patterns.
This document proposes a facial recognition-based attendance system for schools as an alternative to traditional ID card-based systems. It discusses collecting facial images of students and training a neural network using Haar wavelet and deep learning techniques to recognize faces and mark attendance automatically. The system was tested on 70 students across two classes and showed high accuracy in recognizing students and recording their attendance quickly without human error. Some benefits highlighted include improved security, automation, and elimination of buddy punching or manual errors compared to traditional ID card systems. Overall, the research demonstrates that facial recognition can effectively be used to develop an automated attendance management system for schools.
The document provides instructions for writing a 250-300 word paragraph analyzing a specific point from Okakura Kakuzō's essay "The Range of Ideals" to explain why his thesis that "Asia is one" is problematic. The paragraph should directly engage with one point Okakura makes, provide specific details on its logical or factual mistakes, acknowledge the diversity of Asian nations and cultures, and cite the specific page(s) being referred to.
Ralph Waldo Emerson was an American essayist and philosopher born in 1803 who is considered the father of American literature. He developed the philosophy of transcendentalism and emphasized nonconformity, self-reliance, and finding inspiration from nature. Emerson had a profound influence on writers like Thoreau, Whitman, Hawthorne, Poe, and Dickinson and developed a complicated relationship with Thoreau as his former student and friend.
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This document describes a face recognition attendance system. The system uses face recognition techniques to automatically take attendance by detecting and identifying students' faces from live classroom video streams. It aims to address issues with traditional manual attendance methods, which are tedious and prone to errors. The system works in four stages: data collection, face detection, face preprocessing, and face recognition & attendance updating. Faces are detected using Haar Cascade classifiers and further processed using Local Binary Pattern histograms for recognition. When a known face is identified, the student's attendance is automatically marked. The system is designed to provide a more efficient alternative to manual attendance marking.
This document describes a face recognition attendance system that was designed to automate the manual attendance marking process in colleges and universities. The system uses face recognition techniques including face detection, preprocessing, feature extraction, and recognition to identify students from images captured in the classroom and automatically mark their attendance. It discusses related works on biometric attendance systems using technologies like iris recognition and fingerprints. The system design incorporates a teacher module, student module, and functionality for processing images, extracting features, classifying faces, and updating attendance records. It evaluates the methodology used for face recognition, preprocessing, and non-real time recognition and concludes the automated system helps improve accuracy and speed compared to manual attendance marking.
Comparative Analysis of Face Recognition Methodologies and TechniquesFarwa Ansari
In the field of computer sciences such as
graphics and also analyzing the image and its processing,
face recognition is the most prominent problem due to the
comprehensive variation of faces and the complexity of
noises and image backgrounds. The purpose and working
of this system is that it identifies the face of a person from
the real time video and verifies the person from the images
store in the database. This paper provides a review of the
methodologies and techniques used for face detection and
recognition. Firstly a brief introduction of Facial
Recognition is given then the review of the face
recognition’s working which has been done until now, is
briefly introduced. Then the next sections covered the
approaches, methodologies, techniques and their
comparison. Holistic, Feature based and Hybrid
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This document describes an automated attendance system using face recognition. The system uses image capture to take photos of students entering the classroom. It then uses the Viola-Jones algorithm for face detection and PCA for feature selection and SVM for classification to recognize students' faces and mark their attendance automatically. When compared to traditional attendance methods, this system saves time and helps monitor students. It discusses related work using RFID, fingerprints, and iris recognition for attendance systems. It outlines the proposed system's modules for image capture, face detection, preprocessing, database development, and postprocessing. Finally, it discusses results, conclusions, and opportunities for future work to improve recognition rates under various conditions.
In today’s world student attendance system in any institution is a lengthy process and consuming the time. Usually we know that RFID card identify the student based on the card. But it allows the bogus attendance .This paper based to approach the compact and reliable classroom attendance system by using RFID card and face recognition technique. Here using MATLAB progress to verify the face of the each student exclusively. Then an individual buzzer is used in this paper. It is identifying the proxy attendance and stored in a log file. Then this paper notices the absentees list and sent the message to respective or predefined number. Here RFID and face verified to use and take attendance system that efficiency is 98%.
1. The document discusses using facial recognition technology for ATM security to prevent unauthorized access through stolen cards or PINs. It analyzes existing facial recognition methods like eigenfaces and proposes using 3D recognition to address spoofing issues.
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Face recognition system is very beneficial in real time applications, concentrated in security control systems. Face Detection and Recognition is a vital area in the province of validation. In this project, the Open CV based face recognition strategy has been proposed. This model integrates a camera that captures an input image, an algorithm Haar Cascade Algorithm for detecting face from an input image, identifying the face and marking the attendance in an excel sheet. The proposed system implements features such as detection of faces, extraction of the features, exposure of extracted features, analysis of students attendance, and monthly attendance report generation. Faces are recognized using advanced LBP using the database that contains images of students and is used to identify students using the captured image. Better precision is accomplished in results and the system takes into account the changes that occurs in the face over some time. Ms. Pranitha Prabhakar | Mr. Kathireshan "Face Recognition Based Automated Student Attendance System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38083.pdf Paper URL : https://www.ijtsrd.com/computer-science/other/38083/face-recognition-based-automated-student-attendance-system/ms-pranitha-prabhakar
A PROJECT REPORT ON IRIS RECOGNITION SYSTEM USING MATLABMaria Perkins
1. Iris recognition is a reliable biometric authentication method that uses the unique patterns in the iris to identify individuals.
2. Previous work has focused on detecting fake irises using techniques like analyzing image quality features, extracting texture features from the iris, and applying weighted local binary patterns.
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This document proposes an effective attendance marking system using face recognition and RFID tags. The system aims to automate the attendance marking process. It works as follows:
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This document describes the development of an attendance tracking system using fingerprint recognition and an Android application. Key points:
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This document proposes a facial recognition-based attendance system for schools as an alternative to traditional ID card-based systems. It discusses collecting facial images of students and training a neural network using Haar wavelet and deep learning techniques to recognize faces and mark attendance automatically. The system was tested on 70 students across two classes and showed high accuracy in recognizing students and recording their attendance quickly without human error. Some benefits highlighted include improved security, automation, and elimination of buddy punching or manual errors compared to traditional ID card systems. Overall, the research demonstrates that facial recognition can effectively be used to develop an automated attendance management system for schools.
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Introduction Many institutes like and universities always.pdf
1. Answer: Introduction Many institutes like schools, colleges, and
universities always
Answer:
Introduction
Many institutes like schools, colleges, and universities always face challenges of keeping all
the handwritten records of the student’s attendants in classes for every class/ batch.
Keeping the proxy attendance manually is very tedious and may not be very accurate. For
this technique, every student will have to carry their RFID cards for their attendance
records daily. An old way of undertaking this process is through calling of names or signing
documents which always takes lots of time and is very insecure. Each instruction and school
require a very reliable technique that they can employ in the tracking of the student`s
attendance. Thus to help reduce this hard and tedious work, a technology of attendance
system using face recognition is suggested which makes the use of the pictures of the faces
of the students. This technique of recognizing the student’s faces or a system of verifying
the faces of the student from their IDs/video frames and digital pictures becomes more
accurate.
For the face recognition system to operate effectively, it has to be able to distinguish
between the various faces which are stored in the database which are based on the specified
information of the faces. During the attendance, the face gets checked and the images are
transferred through a Bluetooth system which is employed for proxy attendance. This
system can be referred to as biometric artificial intelligence (AI) because it has the ability to
the identification of students through checking for the patterns in their face forms and
features. The use of facial recognition is a technique of tracking attendance which is the
fastest and the highest efficient way of handling the records of attendances in such
institutions. In comparison to other available techniques which could have been used as
well including fingerprints and voice recognition, but face recognition is the best and the
quickest technique.
To execute a fingerprint -attendance system there must be a mobile device for fingerprint it
will have to do this through the use of the RFID. And the system to have some records,
every student will have RFID cards to the card reader. Despite the fact that the system of
2. face recognition attendance is becoming more common, it is suggested that it should be
developed through the use of a trained artificial neuron network (ANN). The process of this
system will involve face detection in the first stages of the operation of the whole
system. After the image (face) has been detected then it will be recognized. The use of this
technique offers various advantages which will be thoroughly analyzed in the paper, but a
few advantages can be highlighted like, the system is able to capture images from a
relatively far distance of 0.8 m
Aim
The aim of the project is to develop an attendance system using the face recognition
technique.
Objectives
To meet the above aim of the project, the following objectives must be met;
To show a system that helps in detection and recognition of human faces in real-time in
various institutions like in colleges and universities to help mark the student`s attendance
To develop a system that is automated to help improve the operation of older techniques
like calling of names and marking of attendance list manually in papers.
Literature Review
Face And Bio-Metric Based Attendance And Security System Using RFID And Arduino
This project is all about the implementation of an RFID system of attendance which is
integrated with face recognition for the students in learning institutions but it can also be
employed in other fields like in face recognition of employees. With the integration of
biometrics (fingerprint authentication) in the system security of a place can be highly
enhanced. Some audio welcoming messages on the registration of the employee`s
attendance can be introduced. The face recognition system can be coded through the use of
Arduino Uno and the codes can be given as below. Arduino can be employed for detection
and tracking of faces of students is given in the appendix
The Arduino NANO microcontroller is employed in the coding and two servo motors (one
for up and down and the other one for right and left) can be used for tracking the face;
Figure 1: Showing the circuit diagram for Arduino for face recognition and tracking
(https://circuitdigest.com/microcontroller-projects/arduino-face-tracking-robot)
And the output of this operation can be illustrated in the following diagram;
3. Figure 2: Showing the output of face recognition and tracking using Arduino
(https://circuitdigest.com/microcontroller-projects/arduino-face-tracking-robot)
Attendance System Based On Face Recognition
The project focuses on the attendance system improvement in learning institutes like
colleges and schools. Since there are various bottlenecks of taking manual records of
student attendance, such as the cost, inaccuracy, and fake attendance. Thus the face
recognition and biometric technology are employed as it helps in solving these
bottlenecks. Traditionally, the face recognition employed was not as accurate as the ones
used nowadays. The system of attendance using the face recognition technique was very
powerful. In this concept, the images are captured through the closed-circuit television
(CCTV) camera in colleges and schools for the porpoise of attendance. After that the system
will detect the human face through the features in the face like the nose, the eyes, hair, the
mouth as well as various.
There are various techniques that can be employed for face detection like the use of
LBP, Ada-Boast, SNOW, and the SQMT. After face detection, the methods of face recognition
techniques are employed like the HOG (Histogram of oriented Gradient). After this, it will
compare the image captured with the stored image in the database. But if the stored image
does not match with the captured image then it will store such image under the unknown
person database. The outcome of the detection and image recognition can be done using
MATLAB codes. The following are the MATLAB codes for face recognition;
% Create a cascade detector object.
faceDetector = vision.CascadeObjectDetector();
% Read a video frame and run the detector.
videoFileReader = vision.VideoFileReader('visionface.avi');
videoFrame = step(videoFileReader);
bbox = step(faceDetector, videoFrame);
% Draw the returned bounding box around the detected face.
videoOut = insertObjectAnnotation(videoFrame,'rectangle',bbox,'Face');
4. figure, imshow(videoOut), title ('Detected face');
The block diagram below illustrates the working and the process of system of attendance
which is based on face recognition through the;
The features of Haar cascade
HOG
Figure 3: Showing a block diagram for the working and the process of system of attendance
which is based on face recognition (https://www.ijrte.org/wp-
content/uploads/papers/v9i1/A2644059120.pdf)
Automated Attendance Management System Using Face Recognition
The basics behind face recognition is actually the processing of the images. There are
2 key types of image processing;
Digital processing: Digital processing entails manipulation of the digital image content
through the use of a computer or a laptop, this category also has some two subcategories
named:
Automated Attendance system
Manual Attendance system
Analog processing: This is a technology that employs hard copies in manipulation like the
use of printouts and photographs.
There are lots of bottlenecks in dealing with manual attendance such as marking the
absent/ present by pen daily which makes it tedious and maintaining all the marked papers
which are also very cumbersome. But all these problems are solved through the use of an
automated attendance system. There is also some system that could be proposed but they
are affected by serious challenges like the following;
Bluetooth system: Bluetooth system is not scaled and it also needs at least eight connections
at a time.
Biometric-based system: This system scans the unique part of the human body like a
fingerprint to mark attendance but its main challenge is that it takes a lot of time thus it is
time-consuming.
The use of the RFID system: This system operates perfectly by swiping the card to the card
reader but the challenge comes when the card is lost.
Student Attendance Marking Using Face Recognition In The Internet Of Things
5. The key concept in this project is to improve the system attendance using the facial
recognition technique. This will minimize the attendance proxy as well improve the
system`s accuracy;
PIR Sensor: The sensor of Passive Infrared is employed for taking the measurements of the
radiation from the object as well as the object`s motion.
Microcontroller: In this case, Arduino UNO/ Arduino NANO is employed as a controller for
the implementation of the operation together with the use of the sensors.
In this system proposed for face, recognition is conducted through the use of sensors and
Arduino UNO/ NANO microcontroller. The whole concept is put in various steps as below;
In the first step, the students will have to fill their registration form together with all their
details that are stored in the college database. The student’s picture and the image is also
stored in the database; this step is only needed once.
The camera will be set at the class entrance together with sensors and an Arduino
microcontroller. In this situation the PIR sensor is employed for radiation measurements,
the sensor also detects the objects in motions.
While the students enter into their various classes, the first PIR sensor measures the
object`s motion and radiation of the object. And in case the radiation belongs to a human
being then the camera will get activated thus it will capture the picture.
After clicking on the student`s image, the system will have to compare the captured image
and compare it with what is in the database, and if the image matches then it gets updated.
If not matched to what is given in the database, it will be marked absent.
Advantages Of The Proposed System
The proposed design of the system has some benefits which makes it suitable and highly
reliable to be used for attendance system using face recognition technique. Some of these
benefits are discussed below;
This proposed system is capable of handling huge database and the store huge numbers of
images for training.
This system has a higher accurate algorithm that have been employed in many cases as
compared to other algorithms.
Attendance system using face recognition technique system can capture pictures from a
distance of 0.8m distance accurately.
The connectivity of the network is not needed thus no issues such as network problem
while the system is under operation.
The machine interface and Human direct interface is less therefore minimizing lots of errors
which will help in increasing the accuracy to the required extent.
The consumed time for the dataset creation as well as the image training is very less
This system is very simple to operate with higher accuracy for recording attendance of
6. students in class.
The speed of the image capturing is higher good as it also work without getting struck
Methodology
The proposed system of attendance is in four various parts where through the use of the
Webcam will follow to capture the image of the face. The diagram below illustrates the
picture database of a student for the recognition of face and the record of attendance. A
laptop or a computer having an inbuilt web camera is used in this system. For the real-time
images, these pictures need to be used in the creation of the database of the students for the
recognition of the student`s faces. For the verification of the presence of the students in a
class, a laptop will have to snap the images in a real-time video feed for the face of the
student. Then the deep learning of the neural network will be used in the determination of
whether the images of the student`s face match anyone in the database or not. And in case
it matches, the system will then determine how far or how close it matches what is in the
database then it will identify the students by their name thus making an attendance
record. We have various techniques in which this data can be used for example the data can
be used through Microsoft excel.
Figure 4: Showing a suggested system of face recognition
(https://iopscience.iop.org/article/10.1088/1742-6596/2089/1/012078/pdf)
Face Detection
For reasons such as those for security, it is advisable to preserve the faces` images because
the identification of our identities is whole dependent on the recognition of our facial
features as the system eliminated the parts of human`s body There should be a method of
detecting the area of the face automatically , such that if an image having a face can be
generated automatically , then most images of faces should be trimmed for the next
recognition of the face, but not every image can be stored. An efficacy way of realizing this
is through the use of cascade classifier known as the Haar feature based system. This
technology is based on machine learning for the negative and positive pictures which are
employed in training of the cascade functions. It is important to note that we must have all
the resources for the training of the Haar cascade classifier. At this point one can see a
larger version of face which is already identified in a picture for a sixe of 96x 96. The scaled
image will be stored in the system database or it can be processed in real time through a
processor of a real time. The face detection can be illustrated in the following images;
Figure 5: Showing video screen having a green rectangle for the detected face and on the left
is an already cropped face picture for the face recognition
7. (https://iopscience.iop.org/article/10.1088/1742-6596/2089/1/012078/pdf)
Recognition Of Facial Features
There are two ways for face recognition and these include either recognition or database
based on the image detection and the face focused. The key component of the algorithms is
the deep neural network FaceNet which is employed for the conversion of the facial images
to the compact Euclidean space in which there is slight variation in the facial
resemblance. Through the use of FaceNet algorithm, a 128-bit number is generated. A
vector of 128 elements can be generated from the image through dimensional encoding it in
a way that the encoding for two images of a person and also a bigger distance separating
two images of people for the same person having the same encoding. Because the FaceNet
training takes a huge amount of data and time to process hence we need to load the
formerly trained FaceNet to a new inception block of FaceNet which is an already trained
model. The resultant model will have a total of 3743280 and every student will have an
encoding database of 128. In reduction, we can take 10 images of the faces of the students to
help generate 10 encodings. Every student must have his/her own computer for a
dictionary object and have their names as keys, their names and coding are kept in the
encoded information in the Python language. And every student is obliged to be present in
front of the camera (webcam) when the attendance check is being done. The accuracy of
the face recognition is affected by the settings of the threshold so as to make sure that there
is a higher accuracy in the face recognition, the software will fetch a lot of images of one face
and identify each face in real-time and produces a result which is based on the collected
information which has been received from several accolades. The flow chart below
illustrates a process of face recognition
Figure 6: Showing a flow chart for the process of face recognition
(https://iopscience.iop.org/article/10.1088/1742-6596/2089/1/012078/pdf)
The Attendance Marking Procedure
The image of the xlsx file given below illustrates the students` recorded attendance list
where the first column of the file is filled with the names of the students to create the
starting file. In the attendance record, all the columns are used except the first column for a
single class meeting. In case a student has been identified through face recognition for more
than 5 times in a total trial of ten, then the student will be considered to be proficient. And
this will be recorded in a cell which will be linked to every recognition of student`s row and
the class` date is on the column. And because each student who is already identified and has
a number is eligible for the college participation, he or she is then regarded to be in class
while the student without the number (the record of less than five for the ten trials) is
deemed not to be in class;
8. Table 1: Showing lists of students with their score out of 10 trials
The chart below illustrates the scores of the 14 students for a maximum score of 10 in ten
trials of which the students with less than 5 score will be marked as absents while those
with above 5 will be marked as present.
Figure 7: Showing a chart of the score of the students
After the system has been used in marking the attendance using face recognition, what will
be displayed will only be either present or absent but the score of the students will not be
displayed. This can be illustrated using the following chart.
Table 2: Showing the overall mark list as either present or absent
Working System Of The Project
This system is implemented through AdaBoost classifier and Haar features. In this system,
a GUI (graphical user interface) will be developed for storing the roll number and name of
every student in the file. While the gathering of the student`s information is also creating
dataset of the faces of the students and then storing them in a folder. Immediately after the
process of is completed trained images in the folder will be trained for the recognition of
faces. These are some crucial steps in this process of face recognition implementation. For
a real-time, scenario if the camera is put close to the doors of the classrooms it will
continuously capture live images and this is done by capturing live streaming of the same
camera.
The images captured will be compared against the stored images in the dataset at the
registration time. In case the images match the stored images then it will show the student`s
roll number and name which has been recognized or stored. The information will be
automatically stored in the attendance sheet together with the date and the name of the
students. And in case the image capture does not match the ones which are stored in the
database then, it will be marked and stored under an unknown folder.
This system is very useful in recording the attendance of the students in colleges since it
will store the attendance of the student together with the time and date. Thus the members
of faculties in colleges and universities will easily identify who attends the lecture on time.
This suggested attendance system can also be employed in other fields like in laboratory
attendance, organizational attendance, library attendance, and government office
9. attendance. The block diagram below summarizes the working of the proposed attendance
system;
Figure 8: Showing a block diagram for the working of the proposed attendance system
(https://iopscience.iop.org/article/10.1088/1742-6596/2089/1/012078/pdf)
The block diagram in figure 8 above illustrates the flow of the working of the proposed
system. The first student will have to register all their information through the graphical
user interface. The information will be stored in the file after it develops the database of
the student`s face and then trains it. AdaBoost classifier and Haar will be applied. The
system will capture a live image from the video stream and then store the file in the folder.
All the matching images with a score of five and above out of ten trials will be marked
present while those with a score less than five will be marked absent as illustrated in table 2
above.
The system attendance dashboard for the student is illustrated in the following diagram
Figure 9: Showing the student dashboard for the proposed attendance system
(https://iopscience.iop.org/article/10.1088/1742-6596/2089/1/012078/pdf)
From figure 9 above, the system dashboard shows a slot for entering the student`s name
and ID, after feeding these details we must click the icon which is named take Image. After
this the system will take the student`s image as illustrated in the following diagram;
Figure 10: Showing the system taking a photo
(https://iopscience.iop.org/article/10.1088/1742-6596/2089/1/012078/pdf)
Through the use of the AdaBoost and the Haar feature, we need to classify and train the
images. There is also another icon which is named Trained image, thus after capturing the
image, the student will have to click on the Train Image that will have to assign the name
and the ID of the student directly as they have been fed to the system. This can be
illustrated in the following image of the captured image;
10. Figure 11: Showing the system where a student has been recognized
(https://iopscience.iop.org/article/10.1088/1742-6596/2089/1/012078/pdf)
Another key step that needs to be undertaken is tracking images so that we click on the
icon with the name Track Image. Immediately after tracking a specific picture, it will
indicate the student`s ID and name on the recognized image as illustrated in figure 12 below
and after this, the information will get stored in the database. After completion of the steps,
we need to click on the Icon which is named Quit so that the student’s information which is
stored as illustrated in the diagram below. And the system will generate an excel file that
has been stored having the information of the student who is absent and also present in the
class.
Figure 12: Showing the attendance of the student
(https://iopscience.iop.org/article/10.1088/1742-6596/2089/1/012078/pdf)
The whole system can be understood through considering various scenarios which make it
very simple;
When one student enters the classroom then the whole will system operate?
In case one student enters the room of the class at a time then the images of that specific
student will be captured through a camera and then the image will have to be trained to
recognize the student`s face on the basis of the image stored in the dataset. On the
recognition basis of the student`s face illustrated in table 2, the information is related to the
specific image which will create an excel file and store the name of the student name, time,
date, and roll number, the system will mark the student as either absent or present. After
the data has been stored in this face recognition system, the aim of the system will give the
attendance to the absent or present students.
A scenario of multiple classrooms of students entering simultaneously
The system recognizes or detects multiple faces at a time several students enter the
classroom as illustrated in the following diagram;
Figure 13: Showing the face of multiple recognized
(https://iopscience.iop.org/article/10.1088/1742-6596/2089/1/012078/pdf)
After the images have been recognized, then the exit button is clicked and it will show the
attendance notification in the box of attendance. This attendance will be stored in the
11. attendance list automatically together with the name, time, date, and ID of the students.
Python programming language is also a good language which can help implemetatoon of
this proposed attendance system. A good python code which will be used for this can is
given below;
# It helps in identifying the faces
import cv2, sys, numpy, os
size = 4
haar_file = 'haarcascade_frontalface_default.xml'
datasets = 'datasets'
# Part 1: Create fisherRecognizer
print('Recognizing Face Please Be in sufficient Lights...')
# Create a list of images and a list of corresponding names
(images, labels, names, id) = ([], [], {}, 0)
for (subdirs, dirs, files) in os.walk(datasets):
for subdir in dirs:
names[id] = subdir
subjectpath = os.path.join(datasets, subdir)
12. for filename in os.listdir(subjectpath):
path = subjectpath + '/' + filename
label = id
images.append(cv2.imread(path, 0))
labels.append(int(label))
id += 1
(width, height) = (130, 100)
# Create a Numpy array from the two lists above
(images, labels) = [numpy.array(lis) for lis in [images, labels]]
# OpenCV trains a model from the images
# NOTE FOR OpenCV2: remove '.face'
model = cv2.face.LBPHFaceRecognizer_create()
model.train(images, labels)
# Part 2: Use fisherRecognizer on camera stream
face_cascade = cv2.CascadeClassifier(haar_file)
webcam = cv2.VideoCapture(0)
13. while True:
(_, im) = webcam.read()
gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
cv2.rectangle(im, (x, y), (x + w, y + h), (255, 0, 0), 2)
face = gray[y:y + h, x:x + w]
face_resize = cv2.resize(face, (width, height))
# Try to recognize the face
prediction = model.predict(face_resize)
cv2.rectangle(im, (x, y), (x + w, y + h), (0, 255, 0), 3)
if prediction[1]<500:
cv2.putText(im, '% s - %.0f' %
(names[prediction[0]], prediction[1]), (x-10, y-10),
cv2.FONT_HERSHEY_PLAIN, 1, (0, 255, 0))
14. else:
cv2.putText(im, 'not recognized',
(x-10, y-10), cv2.FONT_HERSHEY_PLAIN, 1, (0, 255, 0))
cv2.imshow('OpenCV', im)
key = cv2.waitKey(10)
if key == 27:
break
# It helps in identifying the faces
import cv2, sys, numpy, os
size = 4
haar_file = 'haarcascade_frontalface_default.xml'
datasets = 'datasets'
# Part 1: Create fisherRecognizer
print('Recognizing Face Please Be in sufficient Lights...')
15. # Create a list of images and a list of corresponding names
(images, labels, names, id) = ([], [], {}, 0)
for (subdirs, dirs, files) in os.walk(datasets):
for subdir in dirs:
names[id] = subdir
subjectpath = os.path.join(datasets, subdir)
for filename in os.listdir(subjectpath):
path = subjectpath + '/' + filename
label = id
images.append(cv2.imread(path, 0))
labels.append(int(label))
id += 1
16. (width, height) = (130, 100)
# Create a Numpy array from the two lists above
(images, labels) = [numpy.array(lis) for lis in [images, labels]]
# OpenCV trains a model from the images
# NOTE FOR OpenCV2: remove '.face'
model = cv2.face.LBPHFaceRecognizer_create()
model.train(images, labels)
# Part 2: Use fisherRecognizer on camera stream
face_cascade = cv2.CascadeClassifier(haar_file)
webcam = cv2.VideoCapture(0)
while True:
(_, im) = webcam.read()
17. gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
cv2.rectangle(im, (x, y), (x + w, y + h), (255, 0, 0), 2)
face = gray[y:y + h, x:x + w]
face_resize = cv2.resize(face, (width, height))
# Try to recognize the face
prediction = model.predict(face_resize)
cv2.rectangle(im, (x, y), (x + w, y + h), (0, 255, 0), 3)
if prediction[1]<500:
cv2.putText(im, '% s - %.0f' %
(names[prediction[0]], prediction[1]), (x-10, y-10),
18. cv2.FONT_HERSHEY_PLAIN, 1, (0, 255, 0))
else:
cv2.putText(im, 'not recognized',
(x-10, y-10), cv2.FONT_HERSHEY_PLAIN, 1, (0, 255, 0))
cv2.imshow('OpenCV', im)
key = cv2.waitKey(10)
if key == 27:
break
The above code should be run after the model has been trained for the faces
When the codes are run before training the modes then the following errors are obtained;
For this project, the above python codes can be installed in a system like a laptop or a
computer which will use Webcam to take the images of the students as they enter into their
various classrooms. Through the use of the above python codes, the system will make it
possible to take the faces and compare it with what is stored in the database of which if the
face image is matching with the database it will be marked present but if not it will be
marked as unknown. This project will make it easier to mark attendance register since
those students who are members of the class and their faces are not recognized during that
time will be marked absent. Therefore, this proposed system makes marking easier and
19. more reliable.
The Software System Of The Face Recognition
The experimental arrangement is illustrated in the following diagram of the two databases.
The face image assembling and the image which are mined geographic at the time
procedure of the registration is conducted through the use of the database of the face
recognition.
Figure 14: Showing experimental setup (https://iopscience.iop.org/article/10.1088/1757-
899X/263/4/042095/pdf)
The software operation of the system is illustrated using the algorithm;
Figure 15: Showing the flowchart algorithm for the attendance system operation
(https://iopscience.iop.org/article/10.1088/1757-899X/263/4/042095/pdf)
The algorithm which has been taken is given in the following processes for the algorithm;
Acquisition of the image
Normalization of the Histogram
Filtering of the noise
Skin classification
The first step in this process is to take an image through the use of the camera and there
will be effects of lighting which will be in the captured image. And due to the dissimilar
lighting condition and there will be some noise in the captured images as well then the
image will have to be processed. The process includes the removal of the noise from the
captured images, the elimination of this noise, the medium filter is done through area
histogram normalization. The whole process is illustrated below;
Acquisition Of The Image
The acquisition of an image is also known as the image procurement; the image is settled
which the pixel which is gathered. This can be illustrated using the following diagram;
Figure 16: Showing appearance input (https://iopscience.iop.org/article/10.1088/1757-
20. 899X/263/4/042095/pdf)
Normalization Of The Histogram
The image splitting which is taken through consumes brilliance else dimness which is
employed as a good output of the images captured. The captured image will then get
renovated grey picture the progress;
Figure 17: Showing class images in grey ( https://iopscience.iop.org/article/10.1088/1757-
899X/263/4/042095/pdf)
Figure 18: Showing input for Histogram (https://iopscience.iop.org/article/10.1088/1757-
899X/263/4/042095/pdf)
For the enhancement of dissimilarity in the histogram normalization of the spatial domain
is a good technique to be used. This will assist in the identification of the learners on the
rare. In this case a highly straightforwardly perceived. The obtained images will then get
equalized as illustrated in the following diagram;
Figure 19: Showing equalized images of the histogram
(https://iopscience.iop.org/article/10.1088/1757-899X/263/4/042095/pdf)
Filtering Of Noise
There can be a lot of origin of the sounds in the participation of pictures when they were
taken using a camera. There are lots of techniques that are available but one method
enables classification of the frequent area which can be virtual but it may result in the
removal of some key information in the captured images. An average cleaning of the image
can be employed in this proposed attendance system where noise is dismissed through the
splitting of normalized image. This can be illustrated in the following diagram;
Figure 20: Showing noise filtration (https://iopscience.iop.org/article/10.1088/1757-
899X/263/4/042095/pdf)
Skin Classification
21. This technique is employed to promote the adeptness of the procedure of the identification
of face, it represents a cutoff to effectiveness rise of the procedure of the identification of
face. The process of scanning of faces, jonnes procedure, and viola procedure are recycled
for the exactness and discovering will be developed in case membrane is categorized. The
first Pixel will get systematically connected to transfigures and skin black and white and
this is illustrated in the following diagram;
Figure 21: Showing classification of skin for the detection of
faces (https://iopscience.iop.org/article/10.1088/1757-899X/263/4/042095/pdf)
The technique of face detection realizes the segment faces through sphere shaping on the
pictures which contain the learners` images. The techniques like the Haar classifiers are
employed for face recognition. Immediately after conducting the skin classification, the
degree of the algorithm will be enriched. The algorithm of the detection of the faces will be
first employed for the changeability of the pictures having various actions and were
captured for illumination conditions. This will be applied for the detection of the facial
expression in a phase of audio-visual existent. The first procedure employed is for taking
images and checking the functionality of capturing multiple images in a classroom. The next
step will be the identification of the face images captured. This procedure is recycled to rise
the algorithm speed, The gathered images will then be allotted to bring a distinction drift.
The identified images can be illustrated by the following images;
Figure 22: Showing face detected (https://iopscience.iop.org/article/10.1088/1757-
899X/263/4/042095/pdf)
Conclusion
In summary, the proposed system of attendance system using face recognition technology
is for improving the attendance system in every field like in the organization, colleges,
schools, and companies. Capturing the live images cameras and applying various methods
of face detection as well as the recognition of student faces in colleges will highly help in
traditional and manual work. In this proposed solution through the generation of the
interface, we created the dataset. We have to train the pictures through using Haar cascade
and also through the use of AdaBoost classifier. After training is completed the system will
perfectly recognize and detect faces and non-faces. The images will be stored in the
database will be compared against the stored images in the database. When the image gets
recognized it will be getting updates together with the date and time. Through storage of
the images together with the date and time, it will be very easy for faculty in the colleges
22. and universities to easily keep track of every student present in classes.
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