An Effective Facial Image Verification Based Attendance Management System

As we all know fingerprint recognition is one of the secure and accurate Biometric Technologies. If think about it in deep even with the Biometric system the virus can be spread during these situations. To overcome this, we need to come up with a secure and contactless way of authentication. So, let’s update to some contactless remedies like Iris authentication which are unique for every individual and they don’t need to have any physical contact. So, we can use this Iris detection for a secure and contactless authentication system. The main aim of this research is to provide contactless remedies for students in Educational institutes like Smart Locking system, Attendance management system, and Library Transaction by using their Iris authentication and Face Recognition. Coming to the outline of the attendance management system, we will first collect the data from the Kaggle repository. Next, we split the data into training and testing, then we will train the data using transfer learning techniques and test the model by using test data. Finally, we integrated the trained model with the flask. If the Iris matches then the attendance of a particular person will be posted. If not matched then we train the model by adding new person’s data. For the construction of modern electronic security systems, real-time face recognition is crucial. Face detection, feature extraction, and face recognition are the three procedures involved. After recognizing the face, it will check whether the person’s face matches the collected database. If it matches it will show the person’s name, the number of books he took, and what those books are for Library transactions and in the same way the locker will be open if the person’s data is matched. The proposed methods are secure and unique contactless ways of authentication for every individual. So, we can use these detection and authentication systems for secure and contactless applications. It can be successfully used for students in Educational institutes like Smart Locking system, Attendance management system, and Library Transaction by using their Iris authentication and Face Recognition. The Covid-19 infection in society will undoubtedly decline if the proposed argument is implemented.

A facial recognition system is a piece of technology that can match a human face from a database of images. It is often used for robust authentication. An Attendance Management System is a tool used by businesses to track employee time and participation information. Software as a service (SaaS) allows users to access the software via the Internet as a service. Advanced and automated attendance management technologies are being used to address this issue. The problem of proxies and students being counted as present even if they are not is easily resolved with this architecture. This technology records attendance via a live video broadcast. This system's objective is to develop a facial recognition-based class attendance system because the manual technique of taking attendance currently in use is cumbersome and difficult to maintain. The creation of the database, face detection, face recognition, and attendance updating are the four stages of this system. Faces can be recognized in the live-streamed video from the classroom. The attendance will be recorded in a spreadsheet and saved to Google Drive at the end of the session.

Face recognition technology has many implementation roles in the attendance management system. Attendance systems need proper solutions to detect a face in real-time situations using a particular purpose device. Face recognition systems can differentiate human faces based on face features trained in the deep learning model. Although significant advances in face recognition can increase the variety of face conditions in detection, some challenges still exist in face recognition that makes the existing model need to be taken apart and reparametrized. Challenge comes from lighting condition, blurred condition, also face tilt position. This research design a superficial layer of convolutional neural network using a built-in Tensorflow sequential model library. Generally, a transfer learning mechanism is used in object detection, especially in face recognition. This research doesn't use transfer learning because the accuracy of an existing model like the InceptionV1 model gives good accuracy in cross-validation training but gives a significant error in testing the trained face. The attendance management system was built in Flask Web Framework because developed in a Python language environment. The accuracy of the custom model has an average of 88.23%, which is tested with 16 different students, with each student having 48 pictures.

The management of the attendance can be a great burden on the teachers if it is done by hand. To resolve this problem, smart and auto attendance management system is being utilized. But authentication is an important issue in this system. The smart attendance system is generally executed with the help of biometrics. Face recognition is one of the biometric methods to improve this system. Being a prime feature of biometric verification, facial recognition is being used enormously in several such applications, like video monitoring and CCTV footage system, an interaction between computer & humans and access systems present indoors and network security. By utilizing this framework, the problem of proxies and students being marked present even though they are not physically present can easily be solved. The main implementation steps used in this type of system are face detection and recognizing the detected face.This paper proposes a model for implementing an automated attendance management system for students of a class by making use of face recognition technique, by using Eigenface values, Principle Component Analysis (PCA) and Convolutional Neural Network (CNN). After these, the connection of recognized faces ought to be conceivable by comparing with the database containing student's faces. This model will be a successful technique to manage the attendance and records of students.

Face recognition is among the most productive image processing applications and has a pivotal role in the technical field. In the recent time automated face recognition has become a trend and has been developed very much, this is mainly due to two reasons first it is due to availability of modern technologies and second is due to the ability to save time using face recognition in the process of taking attendance of students. Its usage will grow vast in the future as it saves a lot of time. It consumes a lot of time to take attendance manually and few might also fake the attendance, in order to prevent time consumption and avoid faking the attendance. Face recognition is used to identify the person present in the class and mark their attendance , this is done with the help of image or video frame. We proposed an automatic attendance management system using techniques such as PCA algorithm. The face detection and recognition will automatically detect the students in the classroom and mark the attendance by recognizing the person. The traditional process of making attendance and present biometric systems are vulnerable to proxies. This paper is therefore proposed to tackle all these problems. The proposed system makes the use of Haar classifiers, KNN, CNN, SVM, Generative adversarial networks, and Gabor filters. After face recognition attendance reports will be generated and stored in excel format. The system is tested under various conditions like illumination, head movements, the variation of distance between the student and cameras. After vigorous testing overall complexity and accuracy are calculated. The Proposed system proved to be an efficient and robust device for taking attendance in a classroom without any time consumption and manual work. The system developed is cost-efficient and need less installation

Due to the current manual recording of students' classroom attendance by teachers, it is not conducive to later data statistics and analysis. In response to the attendance issue of such students in class, this article proposes a facial recognition algorithm based on FaceNet, which detects targets using multi-scale convolutional feature layers and is suitable for fast object detection; FaceNet maps faces to Euclidean space through deep learning and uses the distance between feature vectors to determine face similarity. Subsequently, a student classroom attendance management system was designed, integrating information collection, facial recognition, random roll call, and data management modules. FaceNet algorithm was used for facial feature extraction and recognition. The experimental results show that the system can effectively achieve classroom attendance management, but attention should be paid to the impact of camera resolution and face size on recognition accuracy. This method ultimately achieved the goal of recording students' classroom attendance, thus achieving the purpose of student classroom management.

Nowadays, face recognition system becomes popular in research area. Face recognition is also used in many application areas such as attendance management system, people tracking system, and access control system. For multi-face recognition, it has still many challenges for detection and recognition because it is not easy to detect multiple faces from one frame and it is also difficult to recognize the faces with poor resolution. Therefore, the main objective of this paper is to get a better accuracy for multi-face recognition by using the combination of FaceNet and Support Vector Machine (SVM). In this proposed system, FaceNet is used for feature extraction by embedding 128 dimensions per face and SVM is used to classify the given training data with the extracted feature of FaceNet. University Classroom Attendance System is applied by the proposed multi-face recognition. The Experimental result show that the proposed approach is good enough for multi-face recognition with an accuracy of 99.6%. It is better than VGG16 model on the same data-set.

In this paper, we present an extensive evaluation of face recognition and verification approaches performed by the European COST Action MULTI-modal Imaging of FOREnsic SciEnce Evidence (MULTI-FORESEE). The aim of the study is to evaluate various face recognition and verification methods, ranging from methods based on facial landmarks to state-of-the-art off-the-shelf pre-trained Convolutional Neural Networks (CNN), as well as CNN models directly trained for the task at hand. To fulfill this objective, we carefully designed and implemented a realistic data acquisition process, that corresponds to a typical face verification setup, and collected a challenging dataset to evaluate the real world performance of the aforementioned methods. Apart from verifying the effectiveness of deep learning approaches in a specific scenario, several important limitations are identified and discussed through the paper, providing valuable insight for future research directions in the field.

In this paper, we propose a framework for low cost secure electronic voting system based on face recognition. Essentially Local Binary Pattern (LBP) is used for face feature characterization in texture format followed by chi-square distribution is used for image classification. Two parallel systems are developed based on smart phone and web applications for face learning and verification modules. The proposed system has two tire security levels by using person ID followed by face verification. Essentially class specific threshold is associated for controlling the security level of face verification. Our system is evaluated three standard databases and one real home based database and achieve the satisfactory recognition accuracies. Consequently our propose system provides secure, hassle free voting system and less intrusive compare with other biometrics.

The issue of human error, inaccurate and time-consuming of manual attendance system gradually magnified the inefficiency and obsolescence of the system. An automatic attendance management system with face detection will increase the efficiency, accuracy, and security of a system in attendance taking task. Real-Time Face Detection Attendance Management System is concerned in finding human faces through real-time with the attendance taken and saved as reference for future. Face detection system is implemented by using Haar Cascades Classifiers for the object detection. Attendance system is managed with a database management system called MySQL using SQL programming language. Real-Time Face Detection Attendance Management System able to detect front face and manage the attendance in real-time. A text box will display for the attendee to enter their name for the purpose of attendance taken when face is detected. The attendance list and image collection in the database will auto increment and update in MySQL. The accuracy of Real-Time Face Detection Attendance Management System is shown with the comparison between different front face condition in different illumination condition. The result is tested with 4 sets of the attendee’s face in different illumination and different front face condition. 94.44% accuracy of the face detection system is shown from the result taken by the system. The image acquisition for every attendee is collected and saved in the right folder. The attendance is taken with the attendee’s name, date and time in attendance database and image is save into face database. Some technology such as anti-spoofing measures, mask detection and deep learning concept are recommended to be added into the system for future improvement

Real-time face detection and recognition using hybrid-information extracted from face space and facial features

Fairness has become an important agenda in computer vision and artificial intelligence. Recent studies have shown that many computer vision models and datasets exhibit demographic biases and proposed mitigation strategies. These works attempt to address accuracy disparity, spurious correlations, or unbalanced representations in datasets in tasks such as face recognition, verification and expression and attribute classification. These tasks, however, all require face detection as the first preprocessing step, and surprisingly, there has been little effort in identifying or mitigating biases in face detection. Biased face detectors themselves pose a threat against fair and ethical AI systems, and their biases may be further passed on to subsequent downstream tasks such as face recognition in a computer vision pipeline. This paper therefore investigates the problem of biases in face detection, focusing on accuracy disparity of detectors between demographic groups including gender, age group, and skin tone. We collect perceived demographic attributes on a popular face detection benchmark dataset, WIDER FACE, report skewed demographic distributions, and compare detection performance between groups. In order to mitigate the biases, we apply three mitigation methods that have been introduced in the recent literature and also propose two novel methods. Experimental results show that these methods are effective in reducing demographic biases. We also discuss how the effectiveness varies by demographic attributes, detection easiness, and multiple detectors, which will shed light on this new topic of addressing face detection bias.

The problem of fake attendance (proxy) is growing day by day. There are many technologies used to develop the attendance system like RFID, biometrics, bar code. Because all these technologies have been used, many students are able to mark the fake attendance (proxy) and some systems are very time consuming. There is a need to develop the riskless and shielded attendance monitoring and management system which is our main motto. The technology which we are going to use in the proposed system is “iBeacon Technology” which is mostly used for indoor positioning system and proximity detection and also beneficial in removing the problem of fake attendance. The newer technology which is found out to interact with hardware is iBeacon. An iBeacon works on Bluetooth Low Energy technology which is used to send a signal in a definite format. The iBeacon devices give accurate results in an indoor system in spite of the influencing factors of radio waves. In iBeacon devices, we can adjust the range of beacon up to which the device can transmit. The range of iBeacon is up to 200 m, which can vary according to the type of iBeacon used. An infinite number of devices can be connected to the iBeacon. The main advantage of the proposed system is to overcome the human intervention and follow the concept of paperless smart work. The accuracy of our proposed system is very high, i.e., 100%, and removes human intervention to almost zero.

Recent year, the method based on face verification has been one of the most vibrant study aspect in face verification field. The problem of face verification can be defined: imputing the picture or video, identifying or validating by using face database. video-video face verification means inputting video setting, verification by using face video data。It has most useful information, including: a lots of pictures of one person, continuity of face in time and space in video, three dimension face model etc. 1.PCA face verification Recent year, the method based on face verification has been one of the most vibrant study aspect in face verification field. The problem of face verification can be defined: imputing the picture or video, identifying or validating by using face database. video-video face verification means inputing video setting, verification by using face video data。It has most useful information, including: a lots of pictures of one person, continuity of face in time and space in video, three dimension face model etc. The way that face information be described in video in present literature can be summarized as follows: vector、matrix probability、 dynamic model 、fashion. daopting modes of probability and fashion need mass swatch which reflect distribution of face, can accurately describe face distribution. Dynamic model can better use time and space information , but the method is compicated, calculation is great. The describe way of imputing vector has a big defect, the randomicity of choose swatch.the way of matrix is comparatively simple, can apply the informatin of discontinuous pictures in time,but how to depict relationship between matrix is a worthful contect. Face character based on PCA (Principal Component Analysis, PCA)is the classic arithmetic in face verification, it is a face face veification and describe technology educe from bases analysis. Face character is a simple 、fast、 useful、 arithmetic based on transform coefficient character. Face verification: compare the given face portrait with the picture stored in computer, determine the face is whether the appointed guy, it is a one to one matching process, usually one person stored more face portrait in different angle in computer. A face verification system based on video including face detect model face portrait pretreatment model face character distill model and face verification model. 1.1PCA face verification Face verification: compare the given face portrait with the picture stored in computer, determine the face is whether the appointed guy, it is a one to one matching process, usually one person stored more face portrait in different angle in computer. A face verification system (3) based on video including face detect model face portrait pretreatment model face character distill model and face verification model,as picture 1.

Face recognition methods, which usually represent face images using holistic or local facial features, rely heavily on alignment. Their performances also suffer a severe degradation under variations in expressions or poses, especially when there is one gallery per subject only. With the easy access to high-resolution (HR) face images nowadays, some HR face databases have recently been developed. However, few studies have tackled the use of HR information for face recognition or verification. In this paper, we propose a pose-invariant face-verification method, which is robust to alignment errors, using the HR information based on pore-scale facial features. A new keypoint descriptor, namely, pore-Principal Component Analysis (PCA)-Scale Invariant Feature Transform (PPCASIFT)-adapted from PCA-SIFT-is devised for the extraction of a compact set of distinctive pore-scale facial features. Having matched the pore-scale features of two-face regions, an effective robust-fitting scheme is proposed for the face-verification task. Experiments show that, with one frontal-view gallery only per subject, our proposed method outperforms a number of standard verification methods, and can achieve excellent accuracy even the faces are under large variations in expression and pose.