Face Detection & Recognition Based Attendence System

This is the Python FYP Face Recognition Attendance System from the machine learning base By Teach Learn School.

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.

Image segmentation is the process of dividing an image into multiple parts. It is typically used to identify objects or other relevant information in digital images. There are many ways to perform image segmentation including Thresholding methods, Color-based segmentation, Transform methods among many others. Alternately edge detection can be used for image segmentation and data extraction in areas such as image processing, computer vision, and machine vision. Image thresholding is a simple, yet effective, way of partitioning an image into a foreground and background. This image analysis technique is a type of image segmentation that isolates objects by converting grayscale images into binary images. Image thresholding is most effective in images with high levels of contrast. Otsu's method, named after Nobuyuki Otsu, is one such implementation of Image Thresholding which involves iterating through all the possible threshold values and calculating a measure of spread for the pixel levels each side of the threshold, i.e. the pixels that either fall in foreground or background. The aim is to find the threshold value where the sum of foreground and background spreads is at its minimum. Edge detection is an image processing technique for finding the boundaries of objects within images. It works by detecting discontinuities in brightness. An image can have horizontal, vertical or diagonal edges. The Sobel operator is used to detect two kinds of edges in an image by making use of a derivative mask, one for the horizontal edges and one for the vertical edges. 1. Introduction 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 class. Examples include upper torsos, pedestrians, and cars. Face-detection algorithms focus on the detection of frontal human faces. It is analogous to image detection in which the image of a person is matched bit by bit. Image matches with the image stores in database. Any facial feature changes in the database will invalidate the matching process. 2. Needs/Problems There have been widely applied many researches related to face recognition system. The system is commonly used for video surveillance, human and computer interaction, robot navigation, and etc. Along with the utilization of the system, it leads to the need for a faster system response, such as robot navigation or application for public safety. A number of classification algorithms have been applied to face recognition system, but it still has a problem in terms of computing time. In this system, computing time of the classification or feature extraction is an important thing for further concern. To improve the algorithmic efficiency of face detection, we combine the eigenface method using Haar-like features to detect both of eyes and face, and Robert cross edge detector to locate the human face position. Robert Cross uses the integral image representation and simple rectangular features to eliminate the need of expensive calculation of multi-scale image pyramid. 3. Objectives Some techniques used in this application are 1. Eigen-face technique 2. KLT Algorithm 3. Parallel for loop in openmp 4. OpenCV for face detection. 5. Further uses of the techniques

Face Recognition Attendence System

Automatic attendance management will replace the manual method, which takes a lot of time consuming and difficult to maintain. In this method the camera is fixed in the classroom and it will capture the image, the faces are detected and then it is recognized with the database and finally the attendance is marked. If the attendance is marked as absent the message about the student's absent is send to their parents.

It will self extract each faces from the group photo you only have to train those images and test it on the attendence checking images excel files will be automatically generated as attendence record

Smart face recognition attendance system using Raspberry PI

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

SPPU FInal Year Project

A python flask based project to make attendence logging an easy task. Powered by OpenCV and python in the backend and Bulma a cool opensource CSS framework based on Flexbox in the frontend.

Python mini project using AI-ML

In the development of any country democracy plays a vital role. Democracy system runs by a leader of the country who is selected by citizen of a country. Citizens have right to choose leader through election. Process of election consumes lots of manpower as well as resources and preparation is started many days before commencement of the election. During this preparation it may happen that involved people make an illegal arrangement with each other and in the existing system there are certain drawbacks such as damage of machines, dummy voting and problem of proper monitoring. Human face recognition plays an important role in applications such as video surveillance, human computer interface, and face image database management. The algorithm constructs eye, mouth, and boundary maps for verifying each face candidate. Face recognition also refers to the psychological process by which humans locate and attend to faces in a visual scene.. It is analogous to image detection in which the image of a person is matched bit by bit. Image matches with the image stores in database. Any facial feature changes in the database will invalidate the matching process. Through this paper we are aiming that voters do not need to wait for longer period of time as they do not have to wait in a queue and there is no time constraint; this system provides mobility for voters. The advantages of this proposed system is that,it is less time consuming compared to existing system. With this system man power can be reduced and there is no need to apply ink on the fingers. The proposed system is highly secure and no chances of data lost and also unlimited number of candidate information is being stored. A voter can vote only once, so voting multiple times or dummy voting shall be prohibited.

Attendance system using face recognition built using python open-CV and flask

face recognition based attendence system

Config files for my GitHub profile.

Online Attendance Management System Using Face Recognition. This system eliminates human intervention, taking attendance of users together by recognizing faces from anywhere in the institution to interact in real-time.

22nd Foundation day Innovation contest at :: BUIE (MAKAUT) ::

Python based project to solve monotonous and slow attendence system problem. Using the concept of face detection and recognition. Still needs lot of Improvement

"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

Project on Face recognition and attendence system

It is an attendence system which recognise the face and mark the attendence.

The Face Recognition Attendance System is an AI-powered application that automates attendance tracking using facial recognition. It detects and identifies students' faces , updates attendance records in Firebase, and displays real-time student information.

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We have implemented attendance management system for lecturer’s and students in a university. It helps to reduce time and effort, especially in the case of large number of students and lectures. The whole system is implemented in Python programming language.

This repo contains code of automated attendance system using face recognition using haarcascade classifier.

Attendance System Using Face Recognition: This project uses Deep Learning (CNN) for real-time face detection and attendance tracking. Built with Python, OpenCV, and TensorFlow, it ensures accurate identification and seamless logging. Scalable for broader security and attendance applications! 😊

This project implements a face recognition system using Python libraries. It leverages OpenCV for face detection and recognition, while SQLite3 stores facial data efficiently. NumPy provides numerical computations for feature extraction.

A very great and useful Internet of things project . Proxy attendance of students can be easily avoided by this system as it using attendance using blinking of eye and then comparing face from camera module to photo from the already available data.

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