Abstract—Face recognition using Eigen faces is an approach to the detection and identification of human faces and then recognizes the person by comparing characteristics of the face to those of known individuals is described. This approach treats face recognition as a two-dimensional recognition problem, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. Face images are projected onto a feature space `face space' that best encodes the variation among known face images. The face space is defined by the `Eigen faces', which are the eigenvectors of the set of faces. They do not necessarily correspond to isolated features such as eyes, ears, and noses. Eigen faces are obtained from Eigen vectors of an image which is a principle component of analysis. The Principal Component Analysis (PCA) is one of the most successful techniques that have been used in image recognition and compression. The main idea of using PCA for face recognition is to express the large 1-D vector of pixels constructed from 2-D facial image into the compact principal components of the feature space. This can be called Eigen space projection. Eigen space is calculated by identifying the eigenvectors of the covariance matrix derived from a set of facial images. Face recognition has many applicable areas. Moreover, it can be categorized into face identification, face classification, or sex determination. The most useful applications contain crowd surveillance, video content indexing, personal identification (ex. driver’s license), and mug Shots matching, entrance security, etc.
Index Terms—Eigen faces, two-dimensional recognition problem, eigenvectors, PCA
Authors are with the Department of Electronics and Communication Engineering, GMR Institute of Technology, Rajam, Srikakulam Dist, AP, India, 532127 (e-mail: prabhakar.t@gmrit.org, suresh.g@gmrit.org, venkatasuman.j@gmrit.org, lalitha.nv@gmrit.org).
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Cite: Prabhakar Telagarapu, Gulivindala Suresh, J. Venkata Suman, and N. V. Lalitha, "Prabhakar Telagarapu, Gulivindala Suresh, J. Venkata Suman, and N. V. Lalitha,"
International Journal of Computer Theory and Engineering vol. 4, no. 1, pp. 81-84, 2012.
