Spatially Adaptive Logarithmic Total Variation Model for Varying Light Face Recognition

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概要

• 論文の詳細を見る

• In this letter, we propose an extension to the classical logarithmic total variation (LTV) model for face recognition under variant illumination conditions. LTV treats all facial areas with the same regularization parameters, which inevitably results in the loss of useful facial details and is harmful for recognition tasks. To address this problem, we propose to assign the regularization parameters which balance the large-scale (illumination) and small-scale (reflectance) components in a spatially adaptive scheme. Face recognition experiments on both Extended Yale B and the large-scale FERET databases demonstrate the effectiveness of the proposed method.

• The Institute of Electronics, Information and Communication Engineersの論文

著者

• Liao Qingmin

  Visual Information Processing Laboratory Department Of Electronic Engineering/graduate School At Shenzhen Tsinghua University

• LI Weifeng

  Visual Information Processing Laboratory, Department of Electronic Engineering/Graduate School at Shenzhen, Tsinghua University

• Wang Biao

  Visual Information Processing Laboratory Department Of Electronic Engineering/graduate School At Shenzhen Tsinghua University

• LI Zhimin

  Luohu Branch, Shenzhen Municipal Public Security Bureau

関連論文

• Two-Stage Block-Based Whitened Principal Component Analysis with Application to Single Sample Face Recognition
• Spatially Adaptive Logarithmic Total Variation Model for Varying Light Face Recognition

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