Algorithmic Analysis of Automatic Attendance System using Facial Recognition

2022 ◽  
Vol 14 (1) ◽  
pp. 0-0
Keyword(s):  
Face Recognition ◽  
Daily Life ◽  
Real Life ◽  
Student Attendance ◽  
Surveillance Camera ◽  
Daily Life Activities ◽  
The Face ◽  
Algorithmic Analysis ◽  
Human Faces ◽  
Face Biometrics

Attendance management can become a tedious task for teachers if it is performed manually.. This problem can be solved with the help of an automatic attendance management system. But validation is one of the main issues in the system. Generally, biometrics are used in the smart automatic attendance system. Managing attendance with the help of face recognition is one of the biometric methods with better efficiency as compared to others. Smart Attendance with the help of instant face recognition is a real-life solution that helps in handling daily life activities and maintaining a student attendance system. Face recognition-based attendance system uses face biometrics which is based on high resolution monitor video and other technologies to recognize the face of the student. In project, the system will be able to find and recognize human faces fast and accurately with the help of images or videos that will be captured through a surveillance camera. It will convert the frames of the video into images so that our system can easily search that image in the attendance database.

scholarly journals Automated Student Attendance Management System Using Face Recognition

2021 ◽  
pp. 187-192
Author(s):  
Prof. Sandhya Ahire ◽  
Rupali Jadhao ◽  
Vaishali Gawai ◽  
Pooja Giri
Keyword(s):  
Face Recognition ◽  
High Resolution ◽  
Real Time ◽  
Computer System ◽  
Digital Camera ◽  
High Definition ◽  
Student Attendance ◽  
The Face ◽  
Human Faces ◽  
Day By Day

Automated face reputation (AFR) technologies have made many improvements inside the converting international. Clever Attendance using actual-Time Face popularity is an actual-international solution that comes with the day after day activities of handling pupil attendance machine. Face recognition-based attendance machine is a process of spotting the student's face for taking attendance through using face bio-metrics primarily based on high-definition display video and different facts generation. In my face recognition assignment, a computer system can be able to find and apprehend human faces speedy and precisely in pictures or movies which might be being captured via a surveillance digital camera. Several algorithms and techniques were advanced for enhancing the performance of face reputation but the idea to be carried out here is deep getting to know. It allows in the conversion of the frames of the video into photos so that the face of the student can be effortlessly recognized for his or her attendance so that the attendance database can be effortlessly pondered automatically. Inside the ever-changing world, automatic face reputation (AFR) systems have finished numerous advancements. Smart Attendance with real-Time Face popularity is a sensible alternative for handling student attendance systems on a day-by-day foundation. Face reputation-primarily based attendance device is a way of recognizing a student's face for the motive of taking attendance, primarily based on high-resolution facial biometrics.

scholarly journals Dynamic & Intelligent Student Attendance Maintenance Based on Face Recognition

2020 ◽  
Vol 9 (5) ◽  
pp. 399-403
Keyword(s):  
Artificial Intelligence ◽  
Face Recognition ◽  
Face Detection ◽  
Student Attendance ◽  
Teachers And Students ◽  
The Face ◽  
Monitoring Camera ◽  
On Line

The proposed system generally results a solution to some of the problems which occurs in colleges and schools by providing a monitoring camera with the help of “Artificial Intelligence (AI)” . The main problem which can be occurred is wastage of time in taking the attendance manually or through any biometric sensors. The next problem which can be solved is to control the usage of electricity in classrooms when students are not in class. When the videos are getting recorded with the help of monitoring cameras, at the same time the head counting and face detection of the students present will also be done. When the strength of the class is zero ,the head counting also results to zero. The electricity can also be saved at the same time when people are not present in the classroom. The face recognition is the easiest process which can be done for marking the attendance, where the attendance is marked automatically. This process also helps to prevent the fake attendance. Face recognition and detection is generally based on line edge mapping to attain the identity of the student and also meets the wants of attendance in the universities and schools. The image of the student is to be captured and checked with the database simultaneously and marks the attendance of the particular student. The video gets recorded all the time and checks whether the student remains in class for the entire period.The attendance marking system with the help of technology is very essential for both the teachers and students.

Playing with power: Kink, race, and desire

Sexualities ◽  
2020 ◽  
pp. 136346072096406
Author(s):  
Michelle Liang
Keyword(s):  
The Netherlands ◽  
Ethnic Minorities ◽  
Women Of Color ◽  
Daily Life ◽  
Real Life ◽  
People Of Color ◽  
Sexual Expression ◽  
Queer Women ◽  
The Face ◽  
Queer Women Of Color

Although the separation between “real life” and “play” appears to reinscribe liberal notions of autonomy, BDSM practitioners actually mobilize this boundary to trouble liberal understandings of the liberal autonomous rational agent. Through understandings desires as inextricable from power, and fetishes as displacements of anxieties, BDSM practices recognize “irrational” desires and multiple, fractured selves. In examining kink practices of queer women of color in the Netherlands, this paper explores the transformative potentials of BDSM for queer people of color, especially in resisting colonial discourses that privilege liberal discourses of agency and conceptualize bodies of color as nonmodern, inferior, exotic, and irrational. In the face of discourses that pit Dutch freedom and sexual expression against ethnic minorities and sexual constraint, marginalized kinksters are forming communities that radically centralize marginalized kink experiences and reject pathologizing discourses, as they critically alter the implications of and possibilities for slippages between daily life and kink.

scholarly journals Realtime Face-Mask Detection on Raspberry Kit

2021 ◽  
Vol 9 (8) ◽  
pp. 2076-2083
Author(s):  
Kalyan Chakravarthi. M
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
Real Life ◽  
Face Mask ◽  
Recognition System ◽  
Primary Concern ◽  
Public Places ◽  
Life Problems ◽  
Promising Area ◽  
The Face

Abstract: Recognition from faces is a popular and significant technology in recent years. Face alterations and the presence of different masks make it too much challenging. In the real-world, when a person is uncooperative with the systems such as in video surveillance then masking is further common scenarios. For these masks, current face recognition performance degrades. Still, difficulties created by masks are usually disregarded. Face recognition is a promising area of applied computer vision . This technique is used to recognize a face or identify a person automatically from given images. In our daily life activates like, in a passport checking, smart door, access control, voter verification, criminal investigation, and many other purposes face recognition is widely used to authenticate a person correctly and automatically. Face recognition has gained much attention as a unique, reliable biometric recognition technology that makes it most popular than any other biometric technique likes password, pin, fingerprint, etc. Many of the governments across the world also interested in the face recognition system to secure public places such as parks, airports, bus stations, and railway stations, etc. Face recognition is one of the well-studied real-life problems. Excellent progress has been done against face recognition technology throughout the last years. The primary concern to this work is about facial masks, and especially to enhance the recognition accuracy of different masked faces. A feasible approach has been proposed that consists of first detecting the facial regions. The occluded face detection problem has been approached using Cascaded Convolutional Neural Network (CNN). Besides, its performance has been also evaluated within excessive facial masks and found attractive outcomes. Finally, a correlative study also made here for a better understanding.

scholarly journals Face recognition attendance system using Local Binary Pattern (LBP)

2019 ◽  
Vol 8 (1) ◽  
pp. 239-245 ◽  
Author(s):  
Shamsul J. Elias ◽  
Shahirah Mohamed Hatim ◽  
Nur Anisah Hassan ◽  
Lily Marlia Abd Latif ◽  
R. Badlishah Ahmad ◽  
...  
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Face Detection ◽  
Local Binary Pattern ◽  
Recognition System ◽  
Automated System ◽  
Student Attendance ◽  
Recognition Method ◽  
Face Recognition System ◽  
The Face

Attendance is important for university students. However, generic way of taking attendance in universities may include various problems. Hence, a face recognition system for attendance taking is one way to combat the problem. This paper will present an automated system that will automatically saves student’s attendance into the database using face recognition method. The paper will elaborate on student attendance system, image processing, face detection and face recognition. The face detection part will be done by using viola-jones algorithm method while the face recognition part will be carried on by using local binary pattern (LBP) method. The system will ensure that the attendance taking process will be faster and more accurate.

scholarly journals Security Implementation by Machine Learning Based Face Recognization with SVM

2021 ◽  
pp. 214-218
Author(s):  
Apurva Yawalikar ◽  
U. W. Hore
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Face Detection ◽  
Detection System ◽  
Object Class ◽  
Detection Systems ◽  
The Face ◽  
Object Class Detection ◽  
Human Faces ◽  
Work Done

Face detection is a computer technology being used in a variety of applications that identifies human faces in digital images. Face detection also refers to the psychological process by which humans locate and attend to faces in a visual scene. Face detection can be regarded as a specific case of object-class detection. In object-class detection, the task is to find the locations and sizes of all objects in an image that belong to a given. As per the various face detection system seen various work done onto the detection with various way. In existing this are get evaluate with the HOG with SVM, which will help us to get the exact value so that it is necessary to implement the system which will more effective and advance. As per the face detection seen there are various face detection systems are implemented. Determining face is easy but recognition is quite typical so that we are proposed machine learning based face recognition with SVM which helps to determine and detect the faces So the proposed system will get integrated with highly efficient and effective SVM model for face recognition. The proposed methodology will help us to implement the face based security implementation in any security system like door lock, mobile screen lock etc.

scholarly journals A Review on Online Classroom Attendance Marking System Using Face Recognition, Python, Computer Vision, and Digital Image Processing

2021 ◽  
Vol 9 (12) ◽  
pp. 2342-2345
Author(s):  
Nandkishor Satpute
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Face Recognition ◽  
Processing Method ◽  
Online Classroom ◽  
Detection Algorithms ◽  
The Neural Network ◽  
Network Concept ◽  
The Face ◽  
Human Faces

Abstract: The face is that the identity of someone. The tactic to appear out this physical feature has seen an exquisite change since the advent of the image processing method. Attendance is monitored in every school, college and library. The regular method for attendance is for teachers to call student name & mark attendance. Nowadays, AI has been explored for computer vision-related applications. So, we use the neural network concept in Face recognition for automatically attendance marking systems. This project will perform the face recognition and face detection algorithms, to generate the computer systems strength of acquiring and recognizing human faces fast, accurately, and precisely in live streams so that the systems can be used in the marking attendance

scholarly journals ANALYSIS OF HUMAN FACE RECOGNITION ALGORITHM USING PCA+FDIT IN IMAGE DATABASE FOR CRIME INVESTIGATION

2013 ◽  
Vol 4 (3) ◽  
pp. 788-796
Author(s):  
V. S. Manjula
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Face Detection ◽  
Skin Color ◽  
Back Propagation ◽  
Real Life ◽  
Hierarchical Architecture ◽  
Detection And Identification ◽  
The Face ◽  
Real Time Application

In general, the field of face recognition has lots of research that have put interest in order to detect the face and to identify it and also to track it. Many researchers have concentrated on the face identification and detection problem by using various approaches. The proposed approach is further very useful and helpful in real time application. Thus the Face Detection, Identification  which is proposed here is used to detect the faces in videos in the real time application by using the FDIT (Face Detection Identification Technique) algorithm. Thus the proposed mechanism is very help full in identifying individual persons who are been involved in the action of robbery, murder cases and terror activities. Although in face recognition the algorithm used is of histogram equalization combined with Back propagation neural network in which we recognize an unknown test image by comparing it with the known training set images that are been stored in the database. Also the proposed approach uses skin color extraction as a parameter for face detection. A multi linear training and rectangular face feature extraction are done for training, identifying and detecting.   Thus the proposed technique   is PCA + FDIT technique configuration only improved recognition for subjects in images are included in the training data.   It is very useful in identify a single person from a group of faces.   Thus the proposed technique is well suited for all kinds faces frame work for face detection and identification. The face detection and identification modules share the same hierarchical architecture. They both consist of two layers of classifiers, a layer with a set of component classifiers and a layer with a single combination classifier.  Also we have taken a real life example and simulated the algorithms in IDL Tool successfully.

Web-Based Modernized Architecture Over Cloud Computing for Facial Extraction and Recognition

2022 ◽  
pp. 394-414
Author(s):  
Mohamed ElSayed ElAraby ◽  
Ahmed M. Anter
Keyword(s):  
Cloud Computing ◽  
Face Recognition ◽  
Primary Source ◽  
Web Pages ◽  
Web Content ◽  
Service Architecture ◽  
Web Based ◽  
Accessible Information ◽  
The Face ◽  
Human Faces

Web content is diverse and is regarded as the primary source of accessible information that can be accessed through reference links. Web facial images are one type of web content that relates to important web pages and is considered important information for individuals. This chapter proposes face recognition as a service architecture that is based on real-world images from the web. The proposed service is implemented as a service for other third parties via cloud computing; additionally, its architecture is built via cloud using virtual machines that can be expanded based on resource demands. Web crawlers crawl web pages and retrieve images for elastic cloud storage. The collected images are then used to remove human faces and prepare the face images for identification and identifying the matched face of the set through successive phases. This chapter used PCA for features extraction and KNN for identification. Experiments show that increasing the number of crawler instances improves crawling speed and improves face recognition accuracy by preferring Euclidean over other metrics.

scholarly journals A smart login system using face detection and recognition by ORB algorithm

2020 ◽  
Vol 20 (2) ◽  
pp. 1078
Author(s):  
Mohammad Jahangir Alam ◽  
Tanjia Chowdhury ◽  
Md. Shahzahan Ali
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Punch Card ◽  
Effective Technique ◽  
Face Images ◽  
Different Types ◽  
Web Camera ◽  
The Face ◽  
Face Detection And Recognition ◽  
Human Faces

<p>We can identify human faces using a web Camera which is known as Face Detection.  This is a very effective technique in computer technology. There are used different types of attendance systems such as log in with the password, punch card, fingerprint, etc. In this research, we have introduced a facial recognition type of biometric system that can identify a specific face by analyzing and comparing patterns of a digital image.  This system is the latest login system based on face detection. Primarily, the device captures the face images and stores the captured images into the specific path of the computer relating the information into a database. When any body tries to enter into any room or premises through this login system, the system captures the image of that particular person and matches the image with the stored image. If this image matches with the stored image then the system allows the person to enter the room or premises, otherwise the system denies entry. This face recognition login system is very effective, reliable and secured. This research has used the Viola and Jones algorithm for face detection and ORB for image matching in face recognition and Java, MySql, OpenCV, and iReport are used for implementation.</p>

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