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Enhanced Face Recognition based on PCA and SVM

by K.venkata Narayana, V.v.r. Manoj, K.swathi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 117 - Number 2
Year of Publication: 2015
Authors: K.venkata Narayana, V.v.r. Manoj, K.swathi

K.venkata Narayana, V.v.r. Manoj, K.swathi . Enhanced Face Recognition based on PCA and SVM. International Journal of Computer Applications. 117, 2 ( May 2015), 40-42. DOI=10.5120/20530-2871

@article{ 10.5120/20530-2871,
author = { K.venkata Narayana, V.v.r. Manoj, K.swathi },
title = { Enhanced Face Recognition based on PCA and SVM },
journal = { International Journal of Computer Applications },
issue_date = { May 2015 },
volume = { 117 },
number = { 2 },
month = { May },
year = { 2015 },
issn = { 0975-8887 },
pages = { 40-42 },
numpages = {9},
url = { },
doi = { 10.5120/20530-2871 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:58:18.278561+05:30
%A K.venkata Narayana
%A V.v.r. Manoj
%A K.swathi
%T Enhanced Face Recognition based on PCA and SVM
%J International Journal of Computer Applications
%@ 0975-8887
%V 117
%N 2
%P 40-42
%D 2015
%I Foundation of Computer Science (FCS), NY, USA

Feature Extraction and classification are important aspects of pattern recognition, computer vision. Principal Component Analysis is a well-known feature extraction and data representation technique. But this method is affected by illumination conditions. The combination o PCA an SVM for face recognition is presented in this paper. Before applying Principal Component Analysis preprocessing o images done by using wavelet transform. After PCA is applied or feature extraction. Support Vector Machine is used or classification. Experiments based on using Indian face database. The new technique achieves better performance than using PCA only.

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Index Terms

Computer Science
Information Sciences


Principal component analysis Preprocessing face recognition SVM