22 Open source projects that are alternatives of or similar to Git-Loss-For-Deep-Face-Recognition

This is a modified and simplified inference engine of davidsandberg's facenet

Deep Learning based Web Application for marking attendance of students by recognizing the student's faces from the surveillance video footage of classroom.

即時人臉辨識(使用OpenCV與FaceNet)

I have published my face related codes in this repository

Pipeline for face detection and matching

Tensorflow Implementation of FaceNet: A Unified Embedding for Face Recognition and Clustering to find the celebrity whose face matches the closest to yours.

Implementation of Facial Recognition System Using Facenet based on One Shot Learning Using Siamese Networks

A python script label faces in group photos using Facenet. 🎉

Realtime Facial recognition system using Siamese neural network

A PyTorch implementation of the 'FaceNet' paper for training a facial recognition model with Triplet Loss using the glint360k dataset. A pre-trained model using Triplet Loss is available for download.

IOT implementation for FaceNet project by David Sandberg https://github.com/davidsandberg/facenet

Real Time Face Recognition using FaceNet and OpenCV

Face feature extract, based on insightface & facenet, implemented in tensorflow c api

Face Recognition with Transfer Learning

Face recognition with deep neural networks.

Face recognition using Tensorflow

facenet recognition and retrieve by using hnswlib and flask, convert tensorflow model to caffe

this repo include paper review, code in face recognition

JavaScript SDK for CompreFace - free and open-source face recognition system from Exadel

Face Recognize application. Using FaceNet and CoreML

Face recognition using facenet

This is a Human Attributes Detection program with facial features extraction. It detects facial coordinates using FaceNet model and uses MXNet facial attribute extraction model for extracting 40 types of facial attributes. This solution also detects Emotion, Age and Gender along with facial attributes.