Deep learning face detection and recognition, implemented by pytorch. (pytorch实现的人脸检测和人脸识别)

[High Performance / MAX 30 FPS] RaspberryPi3(RaspberryPi/Raspbian Stretch) or Ubuntu + Multi Neural Compute Stick(NCS/NCS2) + RealSense D435(or USB Camera or PiCamera) + MobileNet-SSD(MobileNetSSD) + Background Multi-transparent(Simple multi-class segmentation) + FaceDetection + MultiGraph + MultiProcessing + MultiClustering

RetinaFace (Single-stage Dense Face Localisation in the Wild, 2019) implemented (ResNet50, MobileNetV2 trained on single GPU) in Tensorflow 2.0+. This is an unofficial implementation. With Colab.

实现常用基于深度学习的人脸检测算法 华为媒体研究院 图文Caption、OCR识别、图视文多模态理解与生成相关方向工作或实习欢迎咨询 15757172165 https://guanfuchen.github.io/media/hw_zhaopin_20220724_tiny.jpg

a tensorflow implement dsfd face detector

2018 云从人头技术冠军分享方案

Light Face Detection using PyTorch Lightning

Python scripts to detect faces in Python with the BlazeFace Tensorflow Lite models

OpenVINO+NCS2/NCS+MutiModel(FaceDetection, EmotionRecognition)+MultiStick+MultiProcess+MultiThread+USB Camera/PiCamera. RaspberryPi 3 compatible. Async.

Make faces blurred for videos using DNN

Fast anime face detection

Face Traking Eyes using the RPI3 + NCS1/2(Intel Movidius Neural Compute Stick) based on OpenVINO Toolkit

Giriş Seviyesinden İleri Seviyeye Kadar Python ile Görüntü İşleme.. Nesne Tespiti, Nesne Takibi, Yüz Tespiti, Göz Algılama ve Göz Hareketleri Takibi, Beden Tespiti, El Hareketlerini Anlamlandırma, Şerit Takibi, Plaka Okuma vs. gibi resim, video ve gerçek zamanlı örnek uygulamalar

基于深度学习的视频人脸检测

Code of Face-MagNet that was published in WACV18

本项目是利用mtcnn网络和facenet网络实现了一个简单的人脸识别功能。整体流程大致如下：首先利用mtcnn网络进行人脸检测和人脸关键点（5个）提取；接着利用人脸关键点进行人脸校正（仿射变换）；然后将校正之后的人脸图片送入facenet网络进行人脸特征（128维）提取；最后将提取到的人脸特征与底库中的人脸特征进行相似度计算（特征比对），完成人脸识别功能。

A lightweight face-recognition toolbox and pipeline based on tensorflow-lite

[Re-implementation] RetinaFace: Single-stage Dense Face Localisation in the Wild

face-detection-yolov8