期刊名称：International Journal of Computer Technology and Applications
电子版ISSN：2229-6093
出版年度：2012
卷号：3
期号：5
页码：1747-1751
出版社：Technopark Publications
摘要：Face recognition plays an essential role in humanmachine interfaces and naturally an automatic face recognition system is an application of great interest. Although the roots of automatic face recognition trace back to the 1960, a complete system that gives satisfactory results for video streams still remains an open problem. Research in the field has been intensified the last decade due to an increasing number of applications that can apply recognition techniques, such as security systems, ATM machines, “smart rooms” and other human machine interfaces. Elastic Bunch Graph Matching (EBGM) [3] is a feature-based face identification method. The algorithm assumes that the positions of certain fiducial points on the faces are known and stores information about the faces by convolving theimages around the fiducial points with 2D Gabor wavelets of varying size. The results of all convolutions form the Gabor jet for that fiducial point. EBGM treats all images as graphs (called Face Graphs), with each jet forming a node. The training images are all stacked in a structure called the Face Bunch Graph (FBG), which is the model used for identification. For each test image, the first step is to estimate the position of fiducial points on the face based on the known positions of fiducial points in the FBG. Eigenfaces are a set of eigenvectors used in the computer vision problem of human face recognition. The approach of using eigenfaces for recognitionwas developed by Sirovich and Kirby (1987) and used by Turk and Alex Pentland in face classification. It is considered the first successful example of facial recognition technology. The purpose of this paper is the implementation of various methods from Two different families of face recognition algorithms, namely the the EBGM and eigenvalues for biometric face recognition.
