IEEE Trans Image Process - Face recognition by exploring information jointly in space, scale and orientation.

Tópicos

{ featur(1941) imag(1645) propos(1176) }
{ imag(2830) propos(1344) filter(1198) }
{ take(945) account(800) differ(722) }
{ concept(1167) ontolog(924) domain(897) }
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{ method(2212) result(1239) propos(1039) }

Resumo

Information jointly contained in image space, scale and orientation domains can provide rich important clues not seen in either individual of these domains. The position, spatial frequency and orientation selectivity properties are believed to have an important role in visual perception. This paper proposes a novel face representation and recognition approach by exploring information jointly in image space, scale and orientation domains. Specifically, the face image is first decomposed into different scale and orientation responses by convolving multiscale and multiorientation Gabor filters. Second, local binary pattern analysis is used to describe the neighboring relationship not only in image space, but also in different scale and orientation responses. This way, information from different domains is explored to give a good face representation for recognition. Discriminant classification is then performed based upon weighted histogram intersection or conditional mutual information with linear discriminant analysis techniques. Extensive experimental results on FERET, AR, and FRGC ver 2.0 databases show the significant advantages of the proposed method over the existing ones.

Resumo Limpo

inform joint contain imag space scale orient domain can provid rich import clue seen either individu domain posit spatial frequenc orient select properti believ import role visual percept paper propos novel face represent recognit approach explor inform joint imag space scale orient domain specif face imag first decompos differ scale orient respons convolv multiscal multiorient gabor filter second local binari pattern analysi use describ neighbor relationship imag space also differ scale orient respons way inform differ domain explor give good face represent recognit discrimin classif perform base upon weight histogram intersect condit mutual inform linear discrimin analysi techniqu extens experiment result feret ar frgc ver databas show signific advantag propos method exist one

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