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Back Issues of Engineering & Technology in India - From February 2016
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Copyright © 2015
M. S. Thirumalai
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Face Recognition Using PCA and
LDA Algorithm
K. Velkumar and M. Bhavani
Abstract
Security is an important concept in all areas. In computer science, biometrics is used for identification as well as for authentication to provide or control access. Lot of biometric recognitions are available among various biometrics, the face recognition is one of the best approach. For extracting the features of face images the combination of both Linear discriminant analysis and Principal Component Analysis algorithms are used. The ORL database has been used for visible facial images, and CASIA dataset has used for IR facial images. As a result, these combinations of an algorithm provide high recognition rate as well as more security.
Keywords: Linear Discriminant Analysis, Principal Component Analysis
I. INTRODUCTION
Face recognition is used for identifying or verifying the person. Some facial recognition algorithms identify facial features by extracting landmarks, or features, from an image of the subject's face. For example, an algorithm may analyze the relative position, size, and/or shape of the eyes, nose, cheekbones, and jaw.[2] These features are then used to search for other images with matching features.[3] Other algorithms normalize a gallery of face images and then compress the face data, only saving the data in the image that is useful for face recognition. A probe image is then compared with the face data.[4] One of the earliest successful systems[5] is based on template matching techniques[6] applied to a set of salient facial features, providing a sort of compressed face representation. Recognition algorithms can be divided into two main approaches, geometric, which looks at distinguishing features, or photometric, which is a statistical approach that distills an image into values and compares the values with templates to eliminate variances. Popular recognition algorithms include Principal Component Analysis using eigenfaces, Linear Discriminate Analysis, Elastic Bunch Graph Matching using the Fisherface algorithm, the Hidden Markov model, the Multilinear Subspace Learning using tensor representation, and the neuronal motivated dynamic link matching.
This is only the beginning part of the article. PLEASE CLICK HERE TO READ THE ENTIRE ARTICLE IN PRINTER-FRIENDLY VERSION.
K. Velkumar
Assistant Professor in Computer Science & Engineering
Theni Kammavar Sangam College of Technology
Theni 625 534
Tamil Nadu
India
velkumar1982@yahoo.com
M. Bhavani
Assistant Professor in Computer Science & Engineering
Theni Kammavar Sangam College of Technology
Theni 625 534
Tamil Nadu
India
gmbhavani1990@gmail.com
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