Evaluation of face recognition techniques using PCA, wavelets and SVM

Authors:
Ergun Gumus;Niyazi Kilic;Ahmet Sertbas;Osman N. Ucan
Affiliations:
Istanbul University, Engineering Faculty, Computer Eng. Dept., 34320 Avcilar, Istanbul, Turkey;Istanbul University, Engineering Faculty, Electrical and Electronics Eng. Dept., 34320 Avcilar, Istanbul, Turkey;Istanbul University, Engineering Faculty, Computer Eng. Dept., 34320 Avcilar, Istanbul, Turkey;Istanbul University, Engineering Faculty, Electrical and Electronics Eng. Dept., 34320 Avcilar, Istanbul, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

Citing 3
Cited 7

SINBAD automation of scientific discovery: From factor analysis to theory synthesis

Natural Computing: an international journal
Eigenfaces for recognition

Journal of Cognitive Neuroscience
Face recognition: a convolutional neural-network approach

IEEE Transactions on Neural Networks

A multi-dimensional trustworthy behavior monitoring method based on discriminant locality preserving projections

ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
On-line principal component analysis with application to process modeling

Neurocomputing
Down Syndrome Diagnosis Based on Gabor Wavelet Transform

Journal of Medical Systems
Design of face recognition algorithm using PCA -LDA combined for hybrid data pre-processing and polynomial-based RBF neural networks: Design and its application

Expert Systems with Applications: An International Journal
High performance concrete compressive strength forecasting using ensemble models based on discrete wavelet transform

Engineering Applications of Artificial Intelligence
Weighted Modular Image Principal Component Analysis for face recognition

Expert Systems with Applications: An International Journal
Using the idea of the sparse representation to perform coarse-to-fine face recognition

Information Sciences: an International Journal

Quantified Score

Hi-index	12.05

Visualization

Abstract

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.