Displaying 1 to 20 from 21 results

digiKam - Advanced Digital Photo Management

digiKam is an advanced digital photo management application for KDE, which makes importing and organizing digital photos a snap. The photos can be organized in albums which can be sorted chronologically, by directory layout or by custom collections. It provides support to add tag, comment to your images.

facenet - Face recognition using Tensorflow

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.

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.

libface - Face Recognition Library

Libface is a cross platform framework for developing face recognition algorithms and testing its performance. The library uses OpenCV 2.0 and aims to be a middleware for developers that don’t have to include any OpenCV code in order to use face recognition and face detection detection.

Flyimg - Microservice to resize and crop images on the fly

Image resizing, cropping and compression on the fly with the impressive MozJPEG compression algorithm. One Docker container to build your own Cloudinary-like service.You pass the image URL and a set of keys with options, like size or compression. Flyimg will fetch the image, convert it, store it, cache it and serve it. The next time the request comes, it will serve the cached version.

Face Detection For Windows Phone 7

A library for performing face detection on windows phone 7. This library uses the same algorithms and detection models as OpenCV and is written in C# and built for the Windows Phone. Also includes a camera user control that supports automated taking of photos and showing camer...

Troll Face SDK

Application project using the Face SDK for Windows Phone for demonstration purpose

Startrinity.com Silverlight realtime multiple face and feature points detector

StarTrinity.com face detection project makes it easier for Silverlight, .NET, Windows Phone 7 developers to detect faces from any image. Facial tracking algorithm is based on Haar features and adaboost training. It is developed in C#

OpenIMAJ - Open Intelligent Multimedia Analysis for Java

OpenIMAJ is an award-winning set of libraries and tools for multimedia (images, text, video, audio, etc.) content analysis and content generation. OpenIMAJ is very broad and contains everything from state-of-the-art computer vision (e.g. SIFT descriptors, salient region detection, face detection, etc.) and advanced data clustering, through to software that performs analysis on the content, layout and structure of webpages.

VideoFaceDetection - Face Detection with Android

VideoFaceDetection is a simple Android application for Face Detection: http://bytefish.de/blog/face_detection_with_android/.

laravel-face-detect - A Laravel Package for Face Detection

A Laravel Package for Face Detection and Cropping in Images. To extract the Face and save the cropped image use...

OpenCVDemo - Face detection and recognition using OpenCV

The application has a Swing GUI and uses OpenCV for capturing the webcam stream; the face detection/recognition is completely decoupled from the GUI because I need to use these classes for a GUIless application on a raspberry. To test face recognition, you have to choose menu Face Recognition -> Capture new user and take at least 20 photos; then you have to press the "Save images" button adding a USERNAME in the input text and the application will save the images to file system (in the java.io.tmpdir dir where the filename has the format USERNAME_N.jpg); now you have to close the application, copy the images to the /src/main/resources/faces directory and restart the application (sooner or later I'll fix this behaviuor).

bob - Bob is a free signal-processing and machine learning toolbox originally developed by the Biometrics group at Idiap Research Institute, in Switzerland

Bob is a free signal-processing and machine learning toolbox originally developed by the Biometrics group at the Idiap Research Institute, Switzerland. The toolbox is written in a mix of Python and C++ and is designed to be both efficient and reduce development time. It is composed of a reasonably large number of packages that implement tools for image, audio & video processing, machine learning & pattern recognition, and a lot more task specific packages.

ccv-purejs - Pure-JS face detection; nodeified &npmified fork of liuliu's browser version https://github

A fork of the pure-javascript face detection in Liu Liu's CCV library (in branch 'current'), converted for Node and npm. This package provides the method detect_objects, to which you pass a parameters hash. The most important parameter is the canvas obj, into which you should have already loaded an image.

face - 👽 Face Recognition package for Laravel

Once installed, you need to register the Face Service provider in your config/app.php. And add Face Facade into config/app.php.

PVANet-FACE - A face detection model based on PVANet

Training a face detection model using PVANet. This repository contains source files of face detection using the PVANet. It is developed based on the awesome pva-faster-rcnn repository.

faced - faced is a light-weight library to identify faces and it's features such as eyes, nose and mouth

faced is a light-weight library for face recognition including features such as eyes, nose and mouth. It requires opencv. Face is outlined in black, the eyes are red & green for left and right respectively, the nose is outlined in white and the mouth in blue.