Author(s): Ashly George, Shameem Kappan, Dr. R. Vijayakumar
Published in: International Journal of Engineering Research & Technology
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Volume/Issue: Volume. 6 - Issue. 08 , August - 2017
Face recognition has been a fast growing, challenging and interesting area in surveillance, image analysis, access control, commercial security and pervasive computing. Face is the primary focused parts of human body that express most of feature which plays vital role to convey identity and emotions of an individual. It is a challenging task to build an automate face recognition system that has capabilities to recognize face as human do. This paper proposes a methodology for evaluation of algorithms for feature extraction in face recognition process. The paper also covers a survey on existing methodologies for face recognition algorithms available in literature namely, Principle Component Analysis, Linear Discriminant Analysis, Kernel Principle Component Analysis and Kernel Linear Discriminant Analysis. For classification the distance classifiers KNN-classifier and Euclidean distance classifiers are employed with ORL database and Faces94 database were to evaluate various degradation in image such as variation in pose, illumination, light effect etc. From the experimental result it is found that Linear Discriminant Analysis provide a better result of 99.68% with Faces94, when number of training set per person is minimum.
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