A Systematic way of Hybrid model design and comparative analysis of EBGM and eigen values for biometric face recognition using neural network Er.Jagmeet Singh Brar Lecturer 9988232197 Govt. Polytechnic College G.T.B Garh Moga,Punjab [email protected]Abstract Face recognition plays an essential role in human- machine 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 the images 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. Er.Sonika Jindal Assistant Professor 9888605641 Shaheed Bhagat Singh College of Engg. & Tech, Ferozepur Punjab,India [email protected]Introduction: For human beings, the task of face identification is fairly straightforward and seemingly uncomplicated; for the average person, only a few glimpses of an unknown face are needed to place it in memory and just as easily recall it when needed. Although humans perform so well in this task, it is not clear how the desired result is achieved; deducing the underlying mechanisms which enable this process is a totally different story, while at the same time being a crucial step in allowing computers to imitate our face recognition capabilities in a reliable and robust manner. When a machine is presented with the face identification problem, it must process a given image or video stream and return the most probable identities of the people present (possibly more than one), according to the contents of its database (i.e. the people the machine “knows”). In an effort to duplicate the human decision process, two main categories of algorithms have been proposed, relying on either information about the whole face or specific, easily-located points on it (facial features). The first of these families of methods is usually termed appearance-based in the literature, whereas the second is referred to as the feature-based approach. Perhaps the best known appearance-based algorithm is the Principal Component Analysis (PCA, [1]), which belongs to the family of Subspace Projection Methods. PCA considers the image as a whole, arranges all pixel values in a line vector and regards each pixel as a separate dimension of the problem. This vector is then projected on a space of much lower dimension (hence the name of the family), in an attempt to reduce the problem size while retaining as much information as possible about the original image. PCA is usually enhanced with Linear Discriminant Analysis (LDA, [2]) in an effort to improve performance. LDA is essentially a Jagmeet Singh Brar et al ,Int.J.Computer Technology & Applications,Vol 3 (5), 1747-1751 IJCTA | Sept-Oct 2012 Available [email protected]1747 ISSN:2229-6093
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A Systematic way of Hybrid model design and … Systematic way of Hybrid model design and comparative analysis of EBGM and eigen values for biometric face recognition using neural
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A Systematic way of Hybrid model design and comparative
analysis of EBGM and eigen values for biometric face