@Article{JICS-10-029,
author = {ZhiyongZeng},
title = {Fusing Shearlets and LBP Feature Sets for Face Recognition},
journal = {Journal of Information and Computing Science},
year = {2024},
volume = {10},
number = {1},
pages = {029--039},
abstract = { To  aim  at  the  challenge  of  face  recognition  to  uncontrolled  situations,  robust  face  recognition 
system  requires  to  take  into  account  different  kinds  of  face  appearance  feature.  However,  most  existing 
methods only use features of just one type. We show that integrating two of global and local representations, 
Shearlets features and local binary pattern (LBP), which gets better performance than either alone. Shearlets 
features not only utilize scale and position information of different scales of decomposed image, but also use 
directional  information.  Shearlets  features  primary  capture  facial  global  attributes  while  LBP  encode  small 
local appearance details. Both feature sets are high dimensional so it is beneficial to apply block-based fisher 
linear discriminant (BFLD) and PCA to reduce dimensionality prior to normalization and integration. Then 
low  dimensional  Shearlets  and  LBP  sets  are  combined  by  score  level  fusion.  the  proposed  method  is 
evaluated on two challenge face databases including MPIE and FERET with promising results. 
},
issn = {1746-7659},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jics/22562.html}
}