Face Masking Model- Google Colab
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Updated
Feb 8, 2023 - Jupyter Notebook
Face Masking Model- Google Colab
Using CNNs to detect face and non-face images (FDDB dataset)
A simple script for detecting a face/faces from an image/images using Python's openCV library.
Attendance Automation using Face Recognition
Face detection using haar cascade classifier, creating pipeline of various models and checking accuracies of models
Face Detection/ bounding box generation using MTCNN library. Further added emotion-detection feature
This project is with the aim of finding faces in an image. Today face detection is an easy progress which we make it easier by preparing a web-app.
This is a project made for the computer vision course HO205 at KU Leuven.
This GitHub repository contains an implementation of a face detection algorithm using MTCNN (Multi-Task Cascaded Convolutional Networks). MTCNN is a state-of-the-art deep learning-based face detection algorithm that is able to accurately detect faces even in challenging environments, such as low light, occlusions, and various angles.
Test repo for python app for face detection
This repo has projects in the field of computer vision
Detect and recognize faces on images or via your camera with python, woohoo!
Extract Face from an image using Haarface cascade
Face Detection Project using Open Cv and Python
Face Detection System
Face detection projects use algorithms and ML to find human faces within larger images, which often incorporate other non-face objects such as landscapes, buildings and other human body parts like feet or hands. Face detection algorithms typically start by searching for human eyes -- one of the easiest features to detect.
This program essentially provides a real-time face detection system using OpenCV.
Automatic Attendance Management System Using Face Recognition is based on machine learning project. In this project 1st step we generate dataset All basic information about students & also take photos sample. 2nd step Trained classifier photos sample. 3rd step Face Recognition detect, analyze & recognize. 4th step Mark Attendance save n database.
Facial Recognition by detecting faces and extracting their embeddings and classifying them into different labels.
This is a student attendance system, implemented with machine learning and face recognition using Local Binary Pattern Histogram-LBPH algorithm. It is implemented in Python using OpenCV.
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