Fusion of local normalization and Gabor entropy weighted features for face identification
作者：
Highlights：
• We propose an entropy weighted strategy over selected Gabor features.
• We fuse local normalization with Gabor features to improve face identification.
• Fusion of different features improve face identification relative to cases with no fusion.
• Our results are compared advantageously on several international face databases.
• We tested our methods with variable illumination, gesticulation, pose and occlusion.
摘要
Highlights•We propose an entropy weighted strategy over selected Gabor features.•We fuse local normalization with Gabor features to improve face identification.•Fusion of different features improve face identification relative to cases with no fusion.•Our results are compared advantageously on several international face databases.•We tested our methods with variable illumination, gesticulation, pose and occlusion.
论文关键词：Face recognition,Face identification,Borda count,Entropy-like weighting,Gabor,LBP,FERET,AR,FRGC 2.0
论文评审过程：Received 9 May 2012, Revised 25 August 2013, Accepted 5 September 2013, Available online 18 September 2013.
论文官网地址：https://doi.org/10.1016/j.patcog.2013.09.003
