University Classroom Attendance System Using FaceNet and Support Vector Machine

Face recognition system is capable of generating a variety of information about a person’s identity quickly and accurately. One of them, face recognition is able to provide information about the gender (male or female) of each person. Gender classification has become an area of extensive research due to its increasing application in existing human-computer interaction (HCI) systems, advertising, biometrics, surveillance systems, content-based indexing and searching. This paper presents face recognition for gender classification using Support Vector Machine (SVM). Support Vector Machine are a system for efficiently training the linear learning machines which can be used for as powerful classification methodology. In this research, we have obtained face recognition accuracy rates for gender classification using Support Vector Machine (SVM) with different kernels. When training data were used 40 to 90 percent, SVM method with RBF kernel and also Polynomial kernel has achieved the same maximum accuracy that is 100 percent.

From the past few years, face recognition has become critical for security and surveillance applications, and is now necessary in many different settings, including offices, educational institutions, airports, corporations, and social spaces. In this paper, we present a framework of multi-face recognition for real time monitoring, resulting in simultaneous face tracking and recognition. First, the faces are detected in the video frames by using viola-jones algorithm. To remove the outliers from the detected face region, we design a face skeleton based on YCBCR color space for further feature points detection and extraction. Then harris corner feature points and SURF feature points are detected from each face, where harris points are used to track the faces in the video and the SURF feature points are used to extract facial features from the cropped faces. As the face tracking is going on, faces are simultaneously recognized by the trained classifier (support vector machine). The experiments conducted on publicly available dataset suggest that our method is reliable, accurate, and robust that can be deployed for real-world multi-face recognition systems.

In this paper, we present a real-time face recognition system for home security service robot which can be applied to recognize the person's face in front and give a warning when the identity of the person is a stranger. Considering the complexity of the actual situation, there might be some errors causing by the following factors like the angle, the size, the environment and the illumination of the human face, which can hardly be avoided. Our system is designed to reduce and eliminate the influence of the factors above. This article first used Local Binary Patterns (LBP) to detect face and cut out the face region. Then, based on the collected and pretreated face database, the target face could be recognized utilizing Support Vector Machine (SVM). However, the given face database are always irregular in size, position of the face area, scale and some other factors. The proposed face recognition system has been tested in practical application for home security service robot. The results indicate that our face recognition system is effective and real-time. It works well in multi-face recognition and stranger identification, which meet the demand of robot.

Face alignment and face recognition are two fundamental problems in the facial analysis community. For face alignment, it forms the basis for the accurate face recognition, age estimation, and facial expression recognition. For face recognition, it has been widely applied in various practical scenarios such as access control system, massive surveillance, human computer interaction, etc.. There mainly exist two lines of works in these two fields, namely holistic face alignment and recognition, and partial face alignment and recognition. Numerous holistic face alignment and recognition works

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.

Support vector machine (SVM) is one of the state-of-the-art tools for linear and nonlinear pattern classification. One of the design issues in SVM classifier is reducing the number of support vectors without compromising the classification accuracy. A technique denoted as diminishing learning (DL) is already proposed in literature for an SVM based multi-class isolated digit recognition system using speaker dependent TI46 database of isolated digits. In this paper, the computational complexity for SVM and SVM-DL based isolated digit recognition system is studied and the computation time for both the classifiers is evaluated by system-on-programmable-chip (SOPC) implementation of the recognition system onto an Altera Cyclone II Series FPGA using Nios II Soft-core processor. The number of support vectors is reduced by 38.28–90.25 % on using SVM-DL for isolated digit recognition problem. This in turn reduces the classification time for SVM-DL by 31.45–91.78 % over SVM. Recognition accuracies of 97 and 98 % are achieved for SVM classifier with and without DL technique, respectively. The study confirms the effect of, the order in which the classes are classified, on the recognition accuracy. For the TI46 database, about 2–4 % increase in recognition accuracy is obtained by choosing the optimum order for SVM-DL classifier. The proposed SOPC implementation of SVM-DL based recognition system can be employed for various other pattern recognition applications too such as face recognition, character recognition and target recognition.

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.

Studies on two-factor authentication based on RFID and face recognition have been carried out on a large scale. However, these studies didn't discuss the way to overcome the weaknesses of face recognition authentication in the access control systems. In this study, two authentication factors, RFID and face recognition, were implemented using the LBP (Local Binary Pattern) algorithm to overcome weaknesses of face recognition authentication in the access control system. Based on the results of performance testing, the access control system has 100% RFID authentication and 80% face recognition authentication. The average time for the RFID authentication process is 0.03 seconds, the face recognition process is 6.3885 seconds and the verification of the face recognition is 0.1970 seconds. The access control system can still work properly after three days without being switched off. The results of security testing showed that the capabilities spoofing detection has 100% overcome the photo attack.

In this paper, we present an effective facial image verification based automated attendance management system (FIV-AMS). The proposed system can be divided into three main components: face detection, image pre-processing, and face recognition. The core step of our attendance management system is the Gist feature extraction based face recognition, which can achieve three functions. Experimental results demonstrate the validity and feasibility of the proposed system by using the statical model and the dynamic model.

Over the years, face recognition has been the research topic that has attracted many researchers around the world. One of the most significant applications of face recognition is the access control system. The access control system allows authorized persons to enter or exit certain or restricted areas. As a result, it will increase the security situation without over-investment in staff security. The access information can be the identification, time, and location, etc. It can be used to carry out human resource management tasks such as attendance and inspection of employees in a more fair and transparent manner. Although face recognition has been widely used in access control systems because of its better accuracy and convenience without requiring too much user cooperation, the 2D-based face recognition systems also retain many limitations due to the variations in pose and illumination. By analyzing facial geometries, 3D facial recognition systems can theoretically overcome the disadvantages of prior 2D methods and improve robustness in different working conditions. In this paper, we propose the 3D facial recognition algorithm for use in an access control system. The proposed algorithm includes the preprocessing, feature extraction, and classification stages. The application of the proposed access control system is the automatic sliding door, the controller of the system, the web-based monitoring, control, and storage of data.

Face recognition technology has become more and more important in the field of biometric identification because of its advantages. A face recognition system combining hardware and software based on S3C2440 is presented in this paper. We adopt S3C2440 embedded development board as the hardware platform. This paper creates an embedded Linux software platform and our system is divided into four sections by their function: graphical user interface, image capture, face detection and face recognition. The whole system is tested as an access control system in practical situation. Experimental results show that our system has a good performance in both accuracy and efficiency of face recognition in access control.

The fast development of modern technologies has led to the frequent usage of face detection and recognition approaches in various fields due to their user-friendly nature. Generally, face recognition approaches identify individuals by their facial features. Recently, numerous face recognition methods were designed by leveraging the significance of deep learning approaches. These approaches are divided into numerous tasks, and implementing these tasks is time consuming as they require real-time images for processing. However, facial features vary across time because of ageing, haircuts, etc. Furthermore, images with poor resolution may also influence the recognition ability of the framework. Therefore, an advanced face recognition framework is designed in this work. The key intent of this designed model is to tackle the obstacles of the classical models and detect individuals in diverse environmental circumstances with better accuracy. Here, the relevant images are collected from a standard data source, and the quality of the image is enhanced using a Retinex-based image enhancement technique. Next, the Generative Adversarial Network (GAN) is employed to synthesize real images that mitigate the effects of pose discrepancies and occlusion, providing clearer features for recognition. Next, synthesized images are fed to Residual network 50 (ResNet50) for face recognition. Finally, the precise face segmentation is done with the support of Gather-Excite Networks of ResUNet (GEN-ResUNet). This integrated approach enhances the accuracy of face recognition, especially in challenging environments with lighting variations and occlusions. The effectiveness of the model is proven by conducting extensive experimentation.

Support vector machine (SVM) is always used for face recognition. However, kernel function selection is a key problem for SVM. This paper tries to make some contributions to this problem with focus on optimizing the parameters in the selected kernel function to improve the accuracy of classification and recognition of SVM. Firstly, an improved artificial fish swarm optimization algorithm (IAFSA) is proposed to optimize the parameters in SVM. In the improved version of artificial fish swarm optimization algorithm, the visual distance and the step size of artificial fish are adjusted adaptively. In the early stage of convergence, artificial fish are widely distributed, and the visual distance and step size take larger values to accelerate the convergence of the algorithm. In the later stage of convergence, artificial fish gathered gradually, and the visual distance and the step size were given small values to prevent oscillation. Then the optimized SVM is used to recognize face images. Simultaneously, in order to improve the accuracy rate of face recognition, an improved local binary pattern (ILBP) is proposed to extract features of face images. Numerical results show the advantage of our new algorithm over a range of existing algorithms.

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.

Facial expressions are important in people’s daily communications. Recognising facial expressions also has many important applications in the areas such as healthcare and e-learning. Existing facial expression recognition systems have problems such as background interference. Furthermore, systems using traditional approaches like SVM (Support Vector Machine) have weakness in dealing with unseen images. Systems using deep neural network have problems such as requirement for GPU, longer training time and requirement for large memory. To overcome the shortcomings of pure deep neural network and traditional facial recognition approaches, this paper presents a new facial expression recognition approach which has image pre-processing techniques to remove unnecessary background information and combines deep neural network ResNet50 and a traditional classifier-- the multiclass model for Support Vector Machine to recognise facial expressions. The proposed approach has better recognition accuracy than traditional approaches like Support Vector Machine and doesn’t need GPU. We have compared 3 proposed frameworks with a traditional SVM approach against the Karolinska Directed Emotional Faces (KDEF) Database, the Japanese Female Facial Expression (JAFFE) Database and the extended Cohn-Kanade dataset (CK+), respectively. The experiment results show that the features extracted from the layer 49Relu have the best performance for these three datasets.