IEEE Trans Image Process - Spatially optimized data-level fusion of texture and shape for face recognition.

Tópicos

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Resumo

Data-level fusion is believed to have the potential for enhancing human face recognition. However, due to a number of challenges, current techniques have failed to achieve its full potential. We propose spatially optimized data/pixel-level fusion of 3-D shape and texture for face recognition. Fusion functions are objectively optimized to model expression and illumination variations in linear subspaces for invariant face recognition. Parameters of adjacent functions are constrained to smoothly vary for effective numerical regularization. In addition to spatial optimization, multiple nonlinear fusion models are combined to enhance their learning capabilities. Experiments on the FRGC v2 data set show that spatial optimization, higher order fusion functions, and the combination of multiple such functions systematically improve performance, which is, for the first time, higher than score-level fusion in a similar experimental setup.

Resumo Limpo

datalevel fusion believ potenti enhanc human face recognit howev due number challeng current techniqu fail achiev full potenti propos spatial optim datapixellevel fusion d shape textur face recognit fusion function object optim model express illumin variat linear subspac invari face recognit paramet adjac function constrain smooth vari effect numer regular addit spatial optim multipl nonlinear fusion model combin enhanc learn capabl experi frgc v data set show spatial optim higher order fusion function combin multipl function systemat improv perform first time higher scorelevel fusion similar experiment setup

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