Verificação facial com Azure Cognitive Services
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Updated
Dec 2, 2018 - Java
Verificação facial com Azure Cognitive Services
A photo gallery with a facial recognition API called face-api.js
Employed Principal Component Analysis to represent input image of faces as a linear Combination of amorphous facial structures called "Ghost Faces" or "Eigenfaces" thereby reducing dimentionality of the data and improving performance
This project is for facial recognition and automatically marking the attendance using python language and KNN Algorithm
Master's Thesis - Using vision transformers for facial recognition
A age and gender identifier machine learning desktop application for CCTV cameras in shopping malls
Lightweight and intuitive package designed to streamline the integration process of dotta biometric service API
Age_Sex and Facial recognition
Chehra - Robust Face Recognition & Gender Classification A comprehensive deep learning solution for face recognition and gender classification under adverse visual conditions. Built for the COMSYS-5 hackathon, this project tackles challenging scenarios with weather disturbances, poor lighting, and visual noise.
An application that will detect object, faces and recognition of people within images and videos
Smile to Win | A Facial Recognition game
Recognize celebrity faces
HW1: Neural Network Assignment. HW2: Handwritten Character Recognition. Project: Facial Recognition
The TASS Facenet uses Siamese Neural Networks and Triplet Loss to classify known and unknown faces by calculating distances between images., and communicates with IoT devices/applications via the free iotJumpWay PaaS
Automatic class attendance system using Facial Recognition API and Opne CV2
A python script for screen time management using facial recognition
🧠 AI-powered facial analysis: Detect age, gender & ethnicity from images using deep learning CNN models. Multi-task learning with TensorFlow/Keras! ✨
Explore facial recognition through an advanced Python implementation featuring Linear Discriminant Analysis (LDA). This repository provides a comprehensive resource, including algorithmic steps, specific ROI code and thorough testing segments, offering professionals a robust framework for mastering and applying LDA in real-world scenarios.
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