Displaying 1 to 20 from 63 results

facenet - Face recognition using Tensorflow

  •    Python

This is a TensorFlow implementation of the face recognizer described in the paper "FaceNet: A Unified Embedding for Face Recognition and Clustering". The project also uses ideas from the paper "Deep Face Recognition" from the Visual Geometry Group at Oxford. The code is tested using Tensorflow r1.7 under Ubuntu 14.04 with Python 2.7 and Python 3.5. The test cases can be found here and the results can be found here.

openface - Face recognition with deep neural networks.

  •    Lua

Free and open source face recognition with deep neural networks. This research was supported by the National Science Foundation (NSF) under grant number CNS-1518865. Additional support was provided by the Intel Corporation, Google, Vodafone, NVIDIA, and the Conklin Kistler family fund. Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and should not be attributed to their employers or funding sources.

DeepVideoAnalytics - A distributed visual search and visual data analytics platform.

  •    Python

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. 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. The query represents a directed acyclic graph of operations.

sphereface - Implementation for <SphereFace: Deep Hypersphere Embedding for Face Recognition> in CVPR'17

  •    Jupyter

SphereFace is released under the MIT License (refer to the LICENSE file for details). 2018.8.14: We recommand an interesting ECCV 2018 paper that comprehensively evaluates SphereFace (A-Softmax) on current widely used face datasets and their proposed noise-controlled IMDb-Face dataset. Interested users can try to train SphereFace on their IMDb-Face dataset. Take a look here.




face_verification_experiment

  •    Matlab

The Deep Face Representation Experiment is based on Convolution Neural Network to learn a robust feature for face verification task. The popular deep learning framework caffe is used for training on face datasets such as CASIA-WebFace, VGG-Face and MS-Celeb-1M. And the feature extraction is realized by python code caffe_ftr.py. The single convolution net testing is evaluated on unsupervised setting only computing cosine similarity for lfw pairs.

face_recognition - The world's simplest facial recognition api for Python and the command line

  •    Python

Recognize and manipulate faces from Python or from the command line with the world's simplest face recognition library. Built using dlib's state-of-the-art face recognition built with deep learning. The model has an accuracy of 99.38% on the Labeled Faces in the Wild benchmark.

face-recognition

  •    Javascript

Simple Node.js API for robust face detection and face recognition. This a Node.js wrapper library for the face detection and face recognition tools implemented in dlib. Installing the package will build dlib for you and download the models. Note, this might take some time.


insightface - Face Recognition Project on MXNet

  •    Python

2018.03.14: train_softmax.py(and a new image_iter.py) is now more clear after removing experimental code. All experimental and unstable test will be put on train.py and data.py. 2018.02.16: We put the MegaFace noise list in this repo. Please refer to [https://github.com/deepinsight/insightface/blob/master/src/megaface] for detail.

fawkes - Fawkes, privacy preserving tool against facial recognition systems

  •    Python

⚠️ Check out our MacOS/Windows Software on our official webpage. Fawkes is a privacy protection system developed by researchers at SANDLab, University of Chicago. For more information about the project, please refer to our project webpage. Contact us at fawkes-team@googlegroups.com.

photonix - A modern, web-based photo management server

  •    Python

This is a photo management application based on web technologies. Run it on your home server and it will let you find what you want from your photo collection using any device. Smart filtering is made possible automatically by object recognition, location awareness, color analysis and other algorithms. This project is currently in development and not feature complete for a version 1.0 yet. If you don't mind putting up with broken parts or want to help out, run the Docker image and give it a go. I'd love for other contributors to get involved.

CompreFace - Leading free and open-source face recognition system

  •    Java

Exadel CompreFace is a free and open-source face recognition service that can be easily integrated into any system without prior machine learning skills. CompreFace provides REST API for face recognition, face verification, face detection, landmark detection, age, and gender recognition and is easily deployed with docker. Exadel CompreFace is a free and open-source face recognition GitHub project. Essentially, it is a docker-based application that can be used as a standalone server or deployed in the cloud. You don’t need prior machine learning skills to set up and use CompreFace.

opencv - OpenCV projects: Face Recognition, Machine Learning, Colormaps, Local Binary Patterns, Examples

  •    C++

This repository contains OpenCV code and documents. More (maybe) here: https://www.bytefish.de.

OpenBR - Open Source Biometric Recognition

  •    C++

OpenBR is a framework for investigating new modalities, improving existing algorithms, interfacing with commercial systems, measuring recognition performance, and deploying automated biometric systems. Off-the-shelf algorithms are also available for specific modalities including Face Recognition, Age Estimation, and Gender Estimation.

EagleEye - Stalk your Friends

  •    Python

This only works if their Facebook Profile is public. You have at least one image of the person you are looking for and a clue about their name. You enter this data into EagleEye and it tries to find Instagram, Youtube, Facebook, and Twitter Profiles of this person.

ownphotos - Self hosted Google Photos clone

  •    Jupyter

Currently the project is in very early stages, so run it only for the sake of checking it out. Ownphotos comes with separate backend and frontend servers. The backend serves the restful API, and the frontend serves, well, the frontend. The easiest way to do it is using Docker.

FaceCropper - :scissors: Crop faces, inside of your image, with iOS 11 Vision api.

  •    Swift

To run the example project, clone the repo, and run pod install from the Example directory first. FaceCropper is available under the MIT license. See the LICENSE file for more info.

LargeMargin_Softmax_Loss - Implementation for <Large-Margin Softmax Loss for Convolutional Neural Networks> in ICML'16

  •    C++

We introduce a large-margin softmax (L-Softmax) loss for convolutional neural networks. L-Softmax loss can greatly improve the generalization ability of CNNs, so it is very suitable for general classification, feature embedding and biometrics (e.g. face) verification. We give the 2D feature visualization on MNIST to illustrate our L-Softmax loss. The paper is published in ICML 2016 and also available at arXiv.

LightCNN - A Light CNN for Deep Face Representation with Noisy Labels, TIFS 2018

  •    Python

A pytorch implementation of A Light CNN for Deep Face Representation with Noisy Labels from the paper by Xiang Wu, Ran He, Zhenan Sun and Tieniu Tan. The official and original Caffe code can be found here. Download face dataset such as CASIA-WebFace, VGG-Face and MS-Celeb-1M.

Facial-Similarity-with-Siamese-Networks-in-Pytorch - Implementing Siamese networks with a contrastive loss for similarity learning

  •    Jupyter

The goal is to teach a siamese network to be able to distinguish pairs of images. This project uses pytorch. Any dataset can be used. Each class must be in its own folder. This is the same structure that PyTorch's own image folder dataset uses.






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