IEEE Trans Image Process - Robust face representation using hybrid spatial feature interdependence matrix.

Tópicos

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Resumo

A key issue in face recognition is to seek an effective descriptor for representing face appearance. In the context of considering the face image as a set of small facial regions, this paper presents a new face representation approach coined spatial feature interdependence matrix (SFIM). Unlike classical face descriptors which usually use a hierarchically organized or a sequentially concatenated structure to describe the spatial layout features extracted from local regions, SFIM is attributed to the exploitation of the underlying feature interdependences regarding local region pairs inside a class specific face. According to SFIM, the face image is projected onto an undirected connected graph in a manner that explicitly encodes feature interdependence-based relationships between local regions. We calculate the pair-wise interdependence strength as the weighted discrepancy between two feature sets extracted in a hybrid feature space fusing histograms of intensity, local binary pattern and oriented gradients. To achieve the goal of face recognition, our SFIM-based face descriptor is embedded in three different recognition frameworks, namely nearest neighbor search, subspace-based classification, and linear optimization-based classification. Extensive experimental results on four well-known face databases and comprehensive comparisons with the state-of-the-art results are provided to demonstrate the efficacy of the proposed SFIM-based descriptor.

Resumo Limpo

key issu face recognit seek effect descriptor repres face appear context consid face imag set small facial region paper present new face represent approach coin spatial featur interdepend matrix sfim unlik classic face descriptor usual use hierarch organ sequenti concaten structur describ spatial layout featur extract local region sfim attribut exploit under featur interdepend regard local region pair insid class specif face accord sfim face imag project onto undirect connect graph manner explicit encod featur interdependencebas relationship local region calcul pairwis interdepend strength weight discrep two featur set extract hybrid featur space fuse histogram intens local binari pattern orient gradient achiev goal face recognit sfimbas face descriptor embed three differ recognit framework name nearest neighbor search subspacebas classif linear optimizationbas classif extens experiment result four wellknown face databas comprehens comparison stateoftheart result provid demonstr efficaci propos sfimbas descriptor

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