Face Recognition FRVT, Face Liveness Detection, Face Recognition , Face Liveness, Face Identification, Face Compare, Face Matching, Face Pose, Face Expression, Face Attributes, Face Landmarks, Face Representation, Face Reconstruction

Face Detection & Recognition Based Attendence System

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

Face recognition, face liveness detection, face matching, face compare, face comparison, face identification, face anti-spoofing, face identity, facial recognition, face representation, face reconstruction, face tracking, and face liveness detection for IDV

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

Face Recognition Attendence System

Taking attendence by face recognition using OpenCV libraries

Attendence Posting Using Face Recognition With OPENCV

Face Recognition Attendence with AWS Rekognition & Raspberry Pi3

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

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.

Smart face recognition attendance system using Raspberry PI

This Repo was created to attend to the Webface260M Masked Face Recognition Challenge

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

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

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

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.

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

face recognition based attendence system

Face Smart An Attendence Systsem Using Python Face Recognition

Experience modern attendance management with our Face Recognition System. Using ESP32-CAM and Arduino, powered by Python's Face Recognition library, we offer accurate and efficient attendance tracking through facial recognition. Goodbye to manual processes, hello to the future.

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.