Open Source Facial Recognition Software - Page 3

Face Recognition web app built on Python using Flask. Students' final project.

OCULUS - is a face recognition system which has a plug-in architecture. This allows to integrate into it new face detection and recognition algorythms easily and make experiments with constructed recognition scenarios.

OmniDJ main public repository This repository contains the following main items: The SotA face recognition engine including facial feature analysis based on : FaceNet - the actual SotA analysis from Deep Face Recognition: A Survey The neural style augmentation of video streams based on Neural Style Transfer: A Review The image augmentation appraoch based on Deep Dream The provided source code is published open-source under Creative Commons. OmniDJ project is financed based on Operational Program for Competitiveness, axis 2.2.1, financing contract 117046/25.05.2017

The development of a biometric system and applications e.g. Access Control based on the system to recognize and verify people. The primary goal is face recognition, but other human attributes might get used too in the future.

Face Detection and Recognition using Intel's OpenCV library

The site says "Malware detected but NOT TRUE", it's just encrypted! Free Source Code Live Face Detection via Web Camera from OpenCvSharp 3.4.5 with CUDA + TBB Original clean source code structure with FREE binary test. reach out .BIN folder for direct tryout(0penCvSharp345.Cuda.x86.FaceDetect.EXE) Other FOLDER contains Face Recognition of OpenCvSharp410, no CUDA! FOR full source code of ****FACE RECOGNITION**** _ OpenCvSharp4.1.0(released date:April_17_2019), 64 Bit,,,, please donate $54 https://www.paypal.me/SoftwareCode/54 mail dbinxecod@gmail.com for direct link download source code

OpenFace is a Python and Torch implementation of face recognition with deep neural networks and is based on the CVPR 2015 paper FaceNet: A Unified Embedding for Face Recognition and Clustering by Florian Schroff, Dmitry Kalenichenko, and James Philbin at Google. Torch allows the network to be executed on a CPU or with CUDA. 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. Accuracies from research papers have just begun to surpass human accuracies on some benchmarks. The accuracies of open source face recognition systems lag behind the state-of-the-art. See our accuracy comparisons on the famous LFW benchmark.

Face recognition project powered by OpenCV, Qt and OpenGL

A modern, web-based photo management server. Run it on your home server and it will let you find the right photo from your collection on any device. Smart filtering is made possible by object recognition, face recognition, location awareness, color analysis and other ML 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. You can move some photos into the folder data/photos and they should get detected and imported immediately. Once you have finished trying out the system you can edit the volume in the docker-compose.yml file where it says ./data/photos to mount wherever you usually keep photos. System database, thumbnails and other cache data is stored separately from the photos so shouldn't pollute the area. You are responsible for keeping your own backups in case of error.

Enhacement of Face Recognition technology, in order to granted some users with their facial texture. user just stand in front of web cam and then application will be granting the user if match with the database.

Capstone project for the facial recognition team at SPSU.

This code uses a technique originally developed for facial recognition to describe shear stress distributions in open channel flow. In this approach, a synthetic database of images representing normalized shear stress distributions is formed from the training data set using recurrence plot analysis. A face recognition algorithm is then employed to synthesize the recurrence plots and transform the original database into short-dimension vectors containing similarity weights proportional to the principal components of the distribution of images. These vectors capture the intrinsic properties of the boundary shear stress distribution of the cases in the training set, and are sensitive to variations of the corresponding hydraulic parameters. The process of transforming one-dimensional data series into vectors of weights is reversible, and therefore, shear stress distributions for unseen cases can be predicted.

StReAV is a software which runs on Linux, loaded onto a PDA/handheld, into which the an audio and video capturing tool which could be easily clamped onto the collar of a shirt is connected. Capture or stream your video, even use it for face recognition.

StarTrinity Face Recognition Library is designed for biometric face login in web-based solutions. The library Allows you to use human face as a password to authorize at your web site Can be integrated into any ASP.NET or PHP web application Is based on Silverlight 4 runtime, detects and verifies face in real time Supports DirectShow-compatible web cameras. Offers the API to detect face, build pattern of face and match face with pattern Uses cloud server to build and store facial patterns Accuracy is up to 99% since version 1.2 (FAR 0%, TRR 99%)

It works as electronic book for keeping attendance records using face identification\recognition technology it can mark attendance upto 100 person it can be used anywhere. MINOR CHANGE: Functionality improved MAJOR CHANGE: 1. Backup and Restore (For 1.1.0 and future versions only) 2. Export your attendance sheets in excel format of each class

The Human Body Project allows computers to better understand and interact with people. Using a webcam the system can detect and recognise individuals (face recognition), identify their gaze direction, facial expressions and upper body postures.

Vision2u offers a free image processing software for personal use and research. Primary tasks of the image processing can be realized during simple operation of the software. Every Web cam owner can have simplest measuring, counting or tasks of monitoring done without high capital outlays.

WHAY is a Video-based Face Recognition tool written in MATLAB. It aims to exploits PCA recognizing as better as possible and tests the limits of this approach.

Artificial neural network with eigenfaces for face recognition

Active Shape/Appearance Model Library (ASMLibrary/AAMLibrary) source code, which includes ASMBuilding/AAMBuilding as well as ASMFitting/AAMFitting algorithm. It is developped under OpenCV 1.0 for locating features in a face and face recognition.

Get different readings including the Tand V of the face also contains complex algorithms like edge detection / grayscale / halftoning / vectorizations / Image Morphing . works on any bmp / jpg images taken from a normal webcam. 3d face recognition.

Face recognition using BPNN. Contains 1.Face recognition using Back propagation neural network (customize code) code using matlab. 2. Face recognition using Back propagation network (builtin) code using matlab.Project closed for now,Adeel Raza Azeemi

1>Face Recognition on real-time based. 2>shirt -color recognition on image based.

Java framework for face detection and face recognition based on different plugin and filter types. Includes Eigenfaces in pure Java, OpenCV detection via JNI, integration of the Betaface.com Web Service, skin color filter, Adobe XMP Export and a nice GUI

iFace is a human face recognition open scource project which developed by python. It is developed based on some code of PyVision project.