IEEE Trans Image Process - Multilayer surface albedo for face recognition with reference images in bad lighting conditions.

Tópicos

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Resumo

In this paper, we propose a multilayer surface albedo (MLSA) model to tackle face recognition in bad lighting conditions, especially with reference images in bad lighting conditions. Some previous researches conclude that illumination variations mainly lie in the large-scale features of an image and extract small-scale features in the surface albedo (or surface texture). However, this surface albedo is not robust enough, which still contains some detrimental sharp features. To improve robustness of the surface albedo, MLSA further decomposes it as a linear sum of several detailed layers, to separate and represent features of different scales in a more specific way. Then, the layers are adjusted by separate weights, which are global parameters and selected for only once. A criterion function is developed to select these layer weights with an independent training set. Despite controlled illumination variations, MLSA is also effective to uncontrolled illumination variations, even mixed with other complicated variations (expression, pose, occlusion, and so on). Extensive experiments on four benchmark data sets show that MLSA has good receiver operating characteristic curve and statistical discriminating capability. The refined albedo improves recognition performance, especially with reference images in bad lighting conditions.

Resumo Limpo

paper propos multilay surfac albedo mlsa model tackl face recognit bad light condit especi refer imag bad light condit previous research conclud illumin variat main lie largescal featur imag extract smallscal featur surfac albedo surfac textur howev surfac albedo robust enough still contain detriment sharp featur improv robust surfac albedo mlsa decompos linear sum sever detail layer separ repres featur differ scale specif way layer adjust separ weight global paramet select criterion function develop select layer weight independ train set despit control illumin variat mlsa also effect uncontrol illumin variat even mix complic variat express pose occlus extens experi four benchmark data set show mlsa good receiv oper characterist curv statist discrimin capabl refin albedo improv recognit perform especi refer imag bad light condit

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