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• W2038961230 startingPage "1903" @default.
• W2038961230 abstract "Humans perform and rely on face recognition routinely and effortlessly throughout their daily lives. Multiple works in recent years have sought to replicate this process in a robust and automatic way. However, it is known that the performance of face recognition algorithms is severely compromised in non-ideal image acquisition scenarios. In an attempt to deal with conditions, such as occlusion and heterogeneous illumination, we propose a new approach motivated by the global precedent hypothesis of the human brain’s cognitive mechanisms of perception. An automatic modeling of SIFT keypoint descriptors using a Gaussian mixture model (GMM)-based universal background model method is proposed. A decision is, then, made in an innovative hierarchical sense, with holistic information gaining precedence over a more detailed local analysis. The algorithm was tested on the ORL, ARand Extended Yale B Face databases and presented state-of-the-art performance for a variety of experimental setups." @default.
• W2038961230 created "2016-06-24" @default.
• W2038961230 creator A5013827007 @default.
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• W2038961230 date "2015-01-16" @default.
• W2038961230 modified "2023-09-25" @default.
• W2038961230 title "A Cognitively-Motivated Framework for Partial Face Recognition in Unconstrained Scenarios" @default.
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• W2038961230 doi "https://doi.org/10.3390/s150101903" @default.
• W2038961230 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4327108" @default.
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• W2038961230 hasPublicationYear "2015" @default.
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