Cheap and reliable Node.js hosting starts at $3/month, and $1/month static HTML hosting

A Free, Offline, Real-Time, Open-source web-app to assist organisers of any event in allowing only authorised/invited people using Face-Recognition Technology or QR Code.

Stars: ✭ 255 (-91.42%)

Mutual labels: face-detection, face-recognition

Arcface Caffe

insightface-caffe

Stars: ✭ 275 (-90.75%)

Mutual labels: face-detection, face-recognition

AISecurityCamera

A simple Security Camera example which detects motion and perform face recognition

Stars: ✭ 27 (-99.09%)

Mutual labels: face-recognition, face-detection

Vggface2 Pytorch

PyTorch Face Recognizer based on 'VGGFace2: A dataset for recognising faces across pose and age'

Stars: ✭ 271 (-90.88%)

Mutual labels: face-detection, face-recognition

facenet-darknet-inference

Face recognition using facenet

Stars: ✭ 29 (-99.02%)

Mutual labels: face-recognition, face-detection

clockon-clockoff-face-recognition

Face Recognize application. Using FaceNet and CoreML

Stars: ✭ 21 (-99.29%)

Mutual labels: face-recognition, face-detection

Multi-Face-Comparison

This repo is meant for backend API for face comparision and computer vision. It is built on python flask framework

Stars: ✭ 20 (-99.33%)

Mutual labels: face-recognition, face-detection

Contactless-Attendance-System

✨ A Contactless Attendance System where your face is identified for Attendance.

Stars: ✭ 20 (-99.33%)

Mutual labels: face-recognition, face-detection

compreface-javascript-sdk

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

Stars: ✭ 19 (-99.36%)

Mutual labels: face-recognition, face-detection

zed face

zedboard上基于FPGA+ARM的人脸识别智能监控系统。关键词：linux，zedboard，arm，fpga，人脸检测，人脸识别。

Stars: ✭ 38 (-98.72%)

Mutual labels: face-recognition, face-detection

Facial-Recognition-Attendance-System

An attendance system which uses facial recognition to detect which people are present in any image.

Stars: ✭ 48 (-98.39%)

Mutual labels: face-recognition, face-detection

lite.ai.toolkit

🛠 A lite C++ toolkit of awesome AI models with ONNXRuntime, NCNN, MNN and TNN. YOLOX, YOLOP, MODNet, YOLOR, NanoDet, YOLOX, SCRFD, YOLOX . MNN, NCNN, TNN, ONNXRuntime, CPU/GPU.

Stars: ✭ 1,354 (-54.46%)

Mutual labels: face-recognition, face-detection

iqiyi-vid-challenge

Code for IQIYI-VID(IQIYI Video Person Identification) Challenge Implemented in Python and MXNet

Stars: ✭ 45 (-98.49%)

Mutual labels: face-recognition, face-detection

shunyaface

Fast Face Recognition on the edge

Stars: ✭ 49 (-98.35%)

Mutual labels: face-recognition, face-detection

pigallery

PiGallery: AI-powered Self-hosted Secure Multi-user Image Gallery and Detailed Image analysis using Machine Learning, EXIF Parsing and Geo Tagging

Stars: ✭ 35 (-98.82%)

Mutual labels: face-recognition, face-detection

PyRecognizer

"A neural network to rule them all, a neural network to find them, a neural network to bring them all and verify if is you !!" (Face recognition tool)

Stars: ✭ 28 (-99.06%)

Mutual labels: face-recognition, face-detection

Recogcis

Face detection & recognition AR app using the mlmodel to recognize company employees.

Stars: ✭ 28 (-99.06%)

Mutual labels: face-recognition, face-detection

View All Similar Projects ➔

Deep Video Analytics

by Akshay Bhat

Jan 2020 Update: I have decided to cease public development of this repo, more infromation is present in Archived.md.

Deep Video Analytics is a platform for indexing and extracting information from videos and images. With latest version of docker installed correctly, you can run Deep Video Analytics in minutes locally (even without a GPU) using a single command.

Installation & Overview

For installation instructions and overview please visit https://www.deepvideoanalytics.com and go through the presentation. The standalone OCR example has been moved to /docs/experiments/ocr directory.

Google Cloud Platform

Deep Video Analytics recommends Google Cloud Platform and is configured to work out of box.

Architecture

Deep Video Analytics implements a client-server architecture pattern, where clients can access state of the server via a REST API. For uploading, processing data, training models, performing queries, i.e. mutating the state clients can send DVAPQL (Deep Video Analytics Processing and Query Language) formatted as JSON. Each query represents a directed acyclic graph of operations.

Libraries present in this repository and their licenses

Library	Link to the license
YAD2K	MIT License
AdminLTE2	MIT License
FabricJS	MIT License
Facenet	MIT License
JSFeat	MIT License
MTCNN	MIT License
Insight Face	MIT License
CRNN.pytorch	MIT License
Original CRNN code by Baoguang Shi	MIT License
Object Detector App using TF Object detection API	MIT License
Plotly.js	MIT License
Text Detection CTPN	MIT License
SphereFace	MIT License
Segment annotator	BSD 3-clause
Youtube 8M feature extractor weights	Apache 2.0
LOPQ	Apache 2.0
Open Images Pre-trained network	Apache 2.0
Interval Tree	Apache 2.0

Libraries present in container (/root/thirdparty/)

Library	Link to the license
faiss	BSD + PATENTS License
dlib	Boost Software License

Additional libraries & frameworks

FFmpeg (not linked, called via a Subprocess)
Tensorflow
OpenCV
Numpy
Pytorch
Docker
LMDB
Nvidia-docker
Docker-compose
All packages in requirements.txt
All dependancies installed in CPU Dockerfile & GPU Dockerfile

License & Copyright

Contact

Please contact me for more information.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].

Stars: ✭ 2,973

Visit Git Page 🔗Visit User Page 🔗Visit Issues Page (1) 🔗