Paper Info
Title
An efficient face recognition through combining local features and statistical feature extraction
Abstract
This paper proposes a hybrid method for face recognition using local features and statistical feature extraction methods. First, a dense set of local feature points are extracted in order to represent a facial image. Each local feature point is described by the keypoint descriptor defined by SIFT feature. Then, the statistical feature extraction methods, PCA and LDA, are applied to the set of local feature descriptors in order to find low dimensional features. With the obtained low dimensional feature vectors, we can conduct face recognition task efficiently using a simple classifier. Through computational experiments on benchmark data sets, we show that the proposed method is superior to the conventional PCA and LDA in the classification performance. In addition, we also show that the proposed method can achieve remarkable improvement in the processing time compared to the conventional keypoint matching methods proposed for local features.
Year
DOI
Venue
2010
10.1007/978-3-642-15246-7_42
PRICAI
Keywords
Field
DocType
hybrid method,low dimensional feature vector,low dimensional feature,benchmark data set,local feature descriptors,sift feature,local feature point,efficient face recognition,statistical feature extraction method,local feature,face recognition,feature vector,computer experiment,feature extraction
Scale-invariant feature transform,Computer science,Feature (machine learning),Artificial intelligence,Kanade–Lucas–Tomasi feature tracker,Classifier (linguistics),Computer vision,Facial recognition system,Feature vector,Pattern recognition,Feature (computer vision),Feature extraction,Machine learning
Conference
Volume
ISSN
ISBN
6230
0302-9743
3-642-15245-7
Citations 
PageRank 
References 
2
0.41
10
Authors
2
Name
Order
Citations
PageRank
Dong-Hyun Kim140353.54
Hyeyoung Park219432.70