Open Source C++ Facial Recognition Software

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.

Libface is a cross platform framework for developing face recognition algorithms and testing its performance.

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.

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.

This is project should implement different biometrics algorithms for fingerprint and face recognition.

Embarcadero C++ Builder Face Detection FREE Source Code! Delphi and C++Builder FACE RECOGNITION,.... here: C++Builder: https://sourceforge.net/projects/c-builder-face-recognition/ Delphi: https://sourceforge.net/projects/delphi-face-recognition/

This project aims at developing a face authentication system, using the Eigenfaces, and Eigenfeatures. The Approach is a principal component analysis method, in which a set of characteristic pictures are used to describe the variation between face images.

The purpose of this project is to provide a biometric security solution by using voice print, fingerprint and/or facial recognition along with a password and/or smart card support using AES to protect data. Please read forums for if interested.

An open source face recognition project.

Face Detection and Recognition using Intel's OpenCV library

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.

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