Linked Life Data

19556198

Source:http://linkedlifedata.com/resource/pubmed/id/19556198

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
8
pubmed:dateCreated
2009-7-14
pubmed:abstractText
In the literature of psychophysics and neurophysiology, many studies have shown that both global and local features are crucial for face representation and recognition. This paper proposes a novel face recognition method which exploits both global and local discriminative features. In this method, global features are extracted from the whole face images by keeping the low-frequency coefficients of Fourier transform, which we believe encodes the holistic facial information, such as facial contour. For local feature extraction, Gabor wavelets are exploited considering their biological relevance. After that, Fisher's linear discriminant (FLD) is separately applied to the global Fourier features and each local patch of Gabor features. Thus, multiple FLD classifiers are obtained, each embodying different facial evidences for face recognition. Finally, all these classifiers are combined to form a hierarchical ensemble classifier. We evaluate the proposed method using two large-scale face databases: FERET and FRGC version 2.0. Experiments show that the results of our method are impressively better than the best known results with the same evaluation protocol.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
1057-7149
pubmed:author
pubmed:issnType
Print
pubmed:volume
18
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1885-96
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Hierarchical ensemble of global and local classifiers for face recognition.
pubmed:affiliation
Key Laboratory of Intelligent InformationProcessing, Institute of Computing Technology, Beijing 100190, China. sgshan@jdl.ac.cn
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S., Research Support, Non-U.S. Gov't