In the UiPath Attended Robot Framework you can find a TensorFlow implementation of the face recognizer described in the paper "FaceNet: A Unified Embedding for Face Recognition and Clustering". The project also uses ideas from the paper "Deep Face Recognition" from the Visual Geometry Group at Oxford. The framework allows you to add an extra layer of security in attended scenarios. In order to do that the robot will ask your name and to take few photos. The model gets trained based on the photos provided.

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

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

Python Coded Script able to Take attendence Via Face Recognition using your Webcam and make them markable as an Existing user for the next time while you click on take Attendence, also give your attendence in Excel (CSV) format and captures all the photos in Training folder.

FACE DETECTION AND ATTENDENC-- Face detection is a computer vision problem that involves finding faces in photos. It is a trivial problem for humans to solve and has been solved reasonably well by classical feature-based techniques, such as the cascade classifier

Every bank has a set of HNI(High net worth individual) customers. It's difficult for the bank staff to distinguish these customers from general customers. This project uses face recognition technology to identify HNI customers present in the bank. Whenever an HNI customer is detected, a picture of the customer taken via CCTV(Laptop's webcam is used in the code as of now) camera and also a picture from the database is sent to the bank staff via a notification generated on an android application. The bank staff then compares both these images and has two choices 1) Attend the customer. 2) Reject the notification(Useful if at all any non HNI customer is recognized falsely as an HNI customer).

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.

A project which automates the process of taking attendence using face recognition and AI and designed using kivymd gui

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

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.

School auto attendence check and chat, scheduler with PyQt5, dlib(recognition_face)

Attendence device with NFC card reader and face recognition. Made for Logic design project (CO3091).

Using Advanced Ai features to recognise faces and mark the attendence, it can also be modified for multiple other purposes like face recognition security system

Has an online(.php) platform where teachers can post schedules of their exam, a face recognition script for attendance and a script to show who has to attend and who is present.

The primary goal of this project is to build a face recognition-based attendance monitoring system for students attending college, college staff and employees working in an organization in order to improve and upgrade the current attendance system to make it more efficient and effective than before

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.

Face attendence system using facial recognition and face encoding

Face_Recognition and attendence taker is a AI/ML based project.

Face Recognition based Attendence management System APi using Django and open cv

A secure face authentication attendence system with advanced recognition, liveness detection, and real-time access control.

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

Capture is an online attendence system which uses face recognition along with machine learning and also block chain to store the data.

Face recognition is perfect a perfect solution for the notorious kids who don't like to attend their school and colleges

Automatic_attendence_system_using_facial_recognition_python_openCV-with_voice Announcement, This project is related to taking attendance using face recognition. And it will create a new Excel file storing your information If you present, it will mark as present as with date and time For every day, it will create a new Excel sheet

We have used Convolutional based AI models to train the model with the dataset that we have created for three people within the code, where the computer uses its webcam to take your pictures and store in the same directory. The model is trained based on that data and it can detect your face and mark your attendence based on that recognition and also mark the time at which the attendence was taken place. The new data zip file contains the data of the three people that was used to train the model and the tdata is the test data that was used to test the model.

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.

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.