There’s no rule how many components that should be kept for good reconstruction capabilities. The first, is the number of components kept for this Principal Component Analysis. The Eigen recognisor takes two variables. Note that the threshold for classification is different with the Eigen recognisor than it is for the Fisher and LBPH classifiers.
The source code makes some key improvements over the original source both in usability and the way it trains and the use of parallel architecture for multiple face recognition. This article will look into PCA analysis and its application in more detail while discussing the use of parallel processing and the future of it in image analysis. The popularity of face recognition is the fact a user can apply a method easily and see if it is working without needing to know too much about how the process is working. PCA is an ideal method for recognizing statistical patterns in data. The reason that face recognition is so popular is not only its real world application but also the common use of principal component analysis (PCA).
If you have used this wrapper before, please feel free to browse other examples on the EMGU Code Reference page.įace Recognition has always been a popular subject for image processing and this article builds upon the work by Sergio Andrés Gutiérrez Rojas and his original article here. Expand the References folder within the solution explorer, delete the three with yellow warning icons and Add fresh references to them located within the Lib folder. You may start with three warnings for the references not being found. If you are new to this wrapper, see the Creating Your First EMGU Image Processing Project article. For more information on the EMGU wrapper, please visit the EMGU website. This article is designed to be the first in several to explain the use of the EMGU image processing wrapper.
Download Face Recognition v2.4.9 (SourceForge).