Evaluation of face recognition techniques using PCA, wavelets and SVM


Gumus E. , Kilic N. , Sertbas A. , Ucan O. N.

EXPERT SYSTEMS WITH APPLICATIONS, cilt.37, ss.6404-6408, 2010 (SCI İndekslerine Giren Dergi)

  • Cilt numarası: 37 Konu: 9
  • Basım Tarihi: 2010
  • Doi Numarası: 10.1016/j.eswa.2010.02.079
  • Dergi Adı: EXPERT SYSTEMS WITH APPLICATIONS
  • Sayfa Sayısı: ss.6404-6408

Özet

In this study, we present an evaluation of using various methods for face recognition. As feature extracting techniques we benefit from wavelet decomposition and Eigenfaces method which is based on Principal Component Analysis (PCA). After generating feature vectors, distance classifier and Support Vector Machines (SVMs) are used for classification step. We examined the classification accuracy according to increasing dimension of training set, chosen feature extractor-classifier pairs and chosen kernel function for SVM classifier. As test set we used ORL face database which is known as a standard face database for face recognition applications including 400 images of 40 people. At the end of the overall separation task, we obtained the classification accuracy 98.1% with Wavelet-SVM approach for 240 image training set. (C) 2010 Elsevier Ltd. All rights reserved.

In this study, we present an evaluation of using various methods for face recognition. As feature extracting techniques we benefit from wavelet decomposition and Eigenfaces method which is based on Principal Component Analysis (PCA). After generating feature vectors, distance classifier and Support Vector Machines (SVMs) are used for classification step. We examined the classification accuracy according to increasing dimension of training set, chosen feature extractor–classifier pairs and chosen kernel function for SVM classifier. As test set we used ORL face database which is known as a standard face database for face recognition applications including 400 images of 40 people. At the end of the overall separation task, we obtained the classification accuracy 98.1% with Wavelet–SVM approach for 240 image training set.