Java and Python Based Student Security Attendence System, uses the property of Face Recognition and QR Based Attendence Punching by students also sending GSM Arrival Message to guardians. Includes many Admin Functionalites for School Organisation.

Face recognition, as one of the most successful applications of image analysis, has recently gained significant attention, especially during the past several years. Our idea proposes an automatic face recognition attendance system for students using raspberry pi and mini cameras based on deep learning and narrowband Internet of things. The system automatically detects and identifies faces and mark present/absent of students with the help of face detection and it points out the technical challenges of building a face recognition system. The system will automatically update the student’s presence in the class to the student’s database and update all the data to the cloud as well as sends It will message to the guardians of absentees and also to the Head of the department. Face recognition processing, including major components such as face detection, tracking, alignment, and feature extraction, and it points out the technical challenges of building a face recognition system. We focus on the importance of the most successful solutions available so far. The final part of the chapter describes chosen face recognition methods and applications and their potential use in areas not related to face recognition.

I have developed this face recognition Attendence system using Python. It is connected to an SQL database to store student details and attendence information. OpenCV and PIL libraries are used to capture the faces of student and store them in an image file. LBPH Face recognizer algorithm is used which is one of the easiest algorithms for face detection. HaarCascade frontal face algorithm is used which identifies the faces in an image.Tkinter is used to create user interface and datetime library is used to record attendence in real time. This recorded attendence can be exported using an excel or csv file

"Automatic Attendance Monitoring System" To maintain the attendance record in schools colleges or any administrative sector through a conventional method by calling the name of every person is very difficult and time-consuming and even a chance of proxy attendance so the following system is based on face recognition to maintain the attendance record of the students and workers. In our implementation, we have tried to recognize faces and marking down the attendance using Haar Cascades with the 'Local Binary Pattern Histogram' technique. The graphic user interface is designed using the Tkinter library. This monitoring system gives the appropriate tracking of the students attending the schools/ colleges with real-time data stored in a separate file. On the contrary, this is an offline system which is the only limitation for this system. Picture Description: 1. Tkinter GUI of the system - 2. Face recognized using this system - 3. Record stored in real-time

This is Face Recognition based Attendence Mangement System by which we can take attendence by using face of Employees, Students or Staff. first, we have to take datasets of Staff then we have to train these dataset after that we can recognise the faces of those Staff members.

we are going to take attendence for the students using face recognition.

It marks attendence of students using face recognition and system send a message automatically to absent student's parents mobile number

Smart Attend AI is an intelligent attendance management system that uses face recognition technology to automatically detect and record student attendance in real time. The system captures facial images through a webcam, processes them using computer vision techniques, and matches them with pre-stored facial data to identify individuals accurately.

Student Attendence System Using Face Recognition

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Student attendence system based on Face Recognition using Python

An android app using face recognition technique to check students' attendence of a class

This projects reflects the use of open cv for the student attendence using face recognition.

FACE RECOGNITION ATTENDENCE SYSTEM USING MACHINE LEARNING AND HAAR CASCADE CLASSIFIER AND AUTOMATIC ATTENDENCE PRINTED IN EXCEL SHEET WITH DATA AND TIME SPECIFIC DATA AND STUDENT ID AND ROLL NUMBER .

The Smart Attendance System (SMS) is to process live video-stream of students entering their classroom and generate the list of students attending the class using Face recognition.

This project automatically checks student attendence and mark there present if they are added in database .This uses open-cv, face-recognition and tkinter modules to create this. And mysql is used as database to store the database.

This face recognition attendence system in python project uses Haar feature-based cascade classifiers and Open Cv library. excel file is updated with students name and time of entry. A plot is made to visualize to see students who came late(its marked in red).This project has 80% accuracy.

This is a project that makes manual work automatic. For this approach,we used face recognition technique to automatically scan (group scan) all the faces that were already registered in the first step and create a new folder everyday,update the details of students who attended. Automatically, an email of this dynamically created file will be sent to the concerned faculty/officials. LBPH Algorithm is used here for face recognition.

This will enable the automation of the student attendance registration process by the use of face recognition technology. This will eliminate the need for the manual roll call and biometric methods of registering attendance, where the facial input will be achieved through the use of the camera and the attending mark will be automated.

This course is a cooperated class with students at Jilin University in China, King Mongkut's University of Technology Thonburi, in Thailand, and Iowa State University to develop software application. This is class_face_recognition project to help teacher to find out which one doesn't attend the class using the camera. I am responsible for the part of face detection and recognizing. I am also charged with uploading the code using the GIT