System functional requirement describes activities and services that must provide. Face recognition is a topic that is very popular both among beginners and experienced computer vision majors. The best accuracy was gotten using ResNet network (29 convolutional layers pretrained model), and it will be the model that was chosen to work with as it was able to detect all faces correctly in our testing dataset. Image content analysis and pattern recognition are rapidly expanding areas of application today, thanks to the increased efficiency offered by the power of computers. This situation is still a challenge to biometric systems especially facial recognition technology. Finally, the result will be shown at the display board attached with the camera. Without checking if the likelihood of the features is Gaussian, we will take this assumption and see if the results are going to be acceptable in term of accuracy. Facial recognition timing systems will give you precise attendance information and stop employees’ buddy clocking – when employee sing on behalf of his colleague-. Also, it will be an added feature in the security. Then after defining our Triplet Loss function we would like to minimize, we use gradient descent to tune the parameters of the CNN in order to learn encoding that gives small distance to two images of same class and high distance for images in different classes. Admin, Lecturer must logged-in their page. Even though the systems proposed in literature are becoming more robust, reliable and efficient in performing face recognition tasks, several real technical and application aspects in the field are often omitted, or very simplified, making formal and complete use of its performance far from being yet a final solution. The instructor who takes the attendance and admin who is responsible for managing students’ faces in the face’s database. Our system uses facial recognition technology to record the attendance through a high resolution digital camera that detects and recognizes faces and compare the recognize faces with students’ faces images stored in faces database. From the plot below, the value d=0.72 will be chosen as it gives the best accuracy (96.8%). In this project, we have two users responsible for the system [6]. We will use preprocessing techniques to detect, recognize and verify the captured faces like Eigenfaces method. The light on when the camera starts facial recognition because reduce the error, There are screen next to the camera to display the result of attendees and absentees, The system at the end of the class update attendance sheet. Another approach to perform facial recognition consist of using a deep convolutional neural network architecture named Inception, which was responsible for setting the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). As can be seen from the Figure 3.2, 84.2% of the lecturer sees the existing system not suitable for all people like deaf. Software programs, workflow and equipment needed to complete this work were presented with a brief description. Again, dlib have a pre-trained model for predicting and finding some the facial landmarks and then transforming them to the reference coordinates. We have seen how PCA has a very low accuracy for being used as an attendance system, and that it needs many faces to be trained with as input data. Turnout is an AI based Face Recognition solution which uses machine learning algorithms to mark the attendance of the employees.We developed AI powered Face recognition attendance system for improving efficiency of the system and a secure way to taking attendance. This method will allow us to see the distribution of our images in a low dimensional space of two dimensions. “Face recognition rises from the moment that machine started to become more and more intelligent and had the advance of fill in, correct or help the lack of human abilities and senses.” [4]. Therefore, our project will focus on online student attendance. A face recognition system is built for matching human faces with a digital image. They provide application (method) to solve this problem, but in order to use this solution you have to sign a contract with the (NIST) organization and to be a researcher or developer. Once all faces of the training set are converted to their corresponding weight’s vectors, we are able to reproduce the training faces by representing them in the eigenspace. Second, write attendees’ names on a paper then move it to the web page. Innovative and economical attendance management system. These two cases have drawbacks such as in the first case, there is a possibility to disconnect the connection and chose the wrong date. The process will repeat if there are missed faces. Once the recognized face matches a stored image, attendance is marked in attendance database for that person. Calculate absenteeism percentage and send reminder messages to students. The K is the number of neighbours, which must be an odd number to avoid having equal votes. Functionality Supported [5]. Face detection will be performed using Dlib’s CNN model as the documentation insists on the high accuracy of CNN compared to HOG face detector. As shown in the following figure, for the same sample image containing 9 faces, we tried both facial detectors using CPU. Both ways are time consuming and associated with high error scales. If it is a face, then the system searches for eyes, a nose, and a mouth. Gone are the days when records had … Our primary goal is to help the lecturers, improve and organize the process of track and manage student attendance and absenteeism. Then we have to create an python enviroment to run the program. Associated risks with stored data and images. The following table compares some of the biometric technology used lately. 1-System requires users to enter username/password. FaceLogin associates user accounts with a picture, and then, when looking at your webcam, you can login because it detects you on the webcam”. Both extractors gave same result, but the CNN takes about 2 mins when the HOG takes only 6 seconds. The aim of this questionnaire is to determine the satisfaction of the current system. If the Admin, Lecturer and student is not fill correctly, the log in fails. This low performance cannot be tolerated for an attendance system where errors are not allowed. Once we get the weight vector of that unknown face, the next step would be to compare it with all the weight vectors of our training set using the Euclidean distance as a metric. Once the recognized face match a retrieved image, the attendance is marked for that person and the attendance sheet is updated. Lecturer can control the errors and correct it. The aim is to make the CNN learn to convert each image into a vector such that the Euclidian distance between all faces of the same identity is small, and the distance between a pair of faces from different identities is large. We can see that our model is comparable with the state of art verification methods that were tested on the same dataset. I agree to the privacy policy and terms. Then we split the data into training and testing: 70% or 7 images per subject as training and 30% or 3 images per subject as testing subsets. The following points summarize features will be adopted in the system: An automatic attendance management system is needed tool for huge organizations. As visualizing the embedded images with 128 dimensions is not a very easy task, we will be using t-distributed stochastic neighbour embedding (t-SNE) which is a machine learning algorithm for nonlinear dimensionality reduction developed by Laurens van der Maaten and Geoffrey Ginton. Finally, data collection methods and constraints: We collect data through many ways, one of them is online survey which is Quantitative data collection method. Facial recognition, have two different applications: basic and advanced “. We utilized Gantt chart in figure 1.1, to show a project schedule with the start and finish dates of several tasks of a project and the deadline to submit the project. For us, to solve this issue we suggest to record twins’ attendance manually. The camera will recognize and detect students’ faces. These chapters organized to reflect the scientific steps toward our main objective. For example, if there are 4 faces missed for a bad position while the detecting phase, then this phase will start again to detect the missed faces and recognize them and continue the attending process. [5], A facial recognition system involves the following phases: Face detection, feature extraction, and face recognition as illustrated in Figure 2.1. The margin alpha that should kept must check the following formula: To train the neural network, we generate triplets of images from our dataset. The other is the observations which is Qualitative data collection method. In addition, attendance considered as the biggest issues that may face lecturers in class. The system is developed for deploying an easy and a secure way of taking down attendance. Ultimately what a computer recognizes is pixel values ranging from 0-255. After the collection and saving the process done the trainingSet manager start to extract faces from the image by face detection. Face Recognition using PCA vs Deep Learning. The classifiers that will be introduced after reducing images dimension will be: Linear Discriminant Analysis: It is a method used in statistics, pattern recognition, and machine learning to find a linear combination of features that characterizes or separates two or more classes of objects or events. Advanced facial recognition manages the question on a specific face. Step 5: Completing the Face Recognition based Attendance System. Also, a warning message sends to the student if he passes the allowed number of absence. It uses Artificial Intelligence (AI) based Computer Vision to capture and recognize the face of an employee for attendance. This is one of the drawbacks of the existing system, and there are more such as: To overcome the problems in the existing system, we will develop a face recognition attendance system. The weights associated to each eigenface represent the contribution of that eigenface to reproduction of the face original. Congratulations on successfully creating your very own face recognition based attendance system. Table ‎3.2: Manage student attendance usecase description. In the following, we will be using 40 components. As our machine learning models will need vectors, we use numpy reshape function we transform our data from a (400, 64, 64) array of images into a (400, 4096) vector. Flexibility, Lectures capability of editing attendance records. The face has to be in front of the device to record attendance. The software is becoming more common in every day interactions. The system then stores the image by mapping it into a face coordinate structure. It contains 10 different images of 40 distinct people with 400 face images. Can store more than one image for a user to maximize face detection. Use face recognition to instantly validate an employee or multiple … A Medium publication sharing concepts, ideas and codes. Only an administrator can run FaceLogin and change its settings. The system stores the faces that are detected and automatically marks attendance. We aim to provide a system that will make the attendance process faster and more precisely. In the next subsection, brief overview of the usage,techniques, and methods in facial recognition. This project is a POC web application demonstrating the use of facial recognition for marking attendance built as a part of my PS -1 internship at ViitorCloud Technologies, Ahmedabad. The resulting combination may be used as a linear classifier, or, more commonly, for dimensionality reduction before later classification. Table ‎2.1: Comparison among some of the biometric technologies. Olivetti is a face images dataset that was made between 1992 and 1994 at AT&T Laboratories Cambridge.
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