A Novel Approach to Face Recognition using Image Segmentation based on SPCA-KNN Method
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Date
2013-04
Authors
Kamencay, Patrik
Zachariasova, Martina
Hudec, Robert
Jarina, Roman
Benco, Miroslav
Hlubik, Jan
ORCID
Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
Společnost pro radioelektronické inženýrství
Abstract
In this paper we propose a novel method for face recognition using hybrid SPCA-KNN (SIFT-PCA-KNN) approach. The proposed method consists of three parts. The first part is based on preprocessing face images using Graph Based algorithm and SIFT (Scale Invariant Feature Transform) descriptor. Graph Based topology is used for matching two face images. In the second part eigen values and eigen vectors are extracted from each input face images. The goal is to extract the important information from the face data, to represent it as a set of new orthogonal variables called principal components. In the final part a nearest neighbor classifier is designed for classifying the face images based on the SPCA-KNN algorithm. The algorithm has been tested on 100 different subjects (15 images for each class). The experimental result shows that the proposed method has a positive effect on overall face recognition performance and outperforms other examined methods.
Description
Citation
Radioengineering. 2013, vol. 22, č. 1, s. 92-99. ISSN 1210-2512
http://www.radioeng.cz/fulltexts/2013/13_01_0092_0099.pdf
http://www.radioeng.cz/fulltexts/2013/13_01_0092_0099.pdf
Document type
Peer-reviewed
Document version
Published version
Date of access to the full text
Language of document
en