Using EMGU to perform Principle Component Analysis (PCA) multiple face recognition is achieved. Using .Net Parallel toolbox real time analysis and optimisation is introduced in a user friendly application.
The article hosted by codeproject is designed to be the first in several to explain the use of the EMGU image processing wrapper. For more information on the EMGU wrapper please visit the EMGU website . If you are new to this wrapper see the Creating Your First EMGU Image Processing Project article. As you will start with 3 warnings for the references not being found. Expand the References folder within the solution explorer delete the 3 with yellow warning icons and Add fresh references to them.
Face Recognition has always been a popular subject for image processing and this article builds upon the good work by Sergio Andrés Gutiérrez Rojas and his original article here: http://www.codeproject.com/KB/audio-video/FaceRecPro.aspx. The reason that face recognition is so popular is not only it’s real world application but also the common use of principle component analysis (PCA). PCA is an ideal method for recognising statistical patterns in data. 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 to much about how the process is working.
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’s in image analysis. 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.
The newest V2.4 is compiled using EMGU 2.4.2, with this version the ability to save a eigen recogniser has been included. There is an extension to the Eigen class that allows the recognition of unknown individuals, a highly requested feature.
The full article and further information is available here:
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