Fusing Shearlets and LBP Feature Sets for Face Recognition
Cited by
Export citation
- BibTex
- RIS
- TXT
@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}
}
TY - JOUR
T1 - Fusing Shearlets and LBP Feature Sets for Face Recognition
AU - ZhiyongZeng
JO - Journal of Information and Computing Science
VL - 1
SP - 029
EP - 039
PY - 2024
DA - 2024/01
SN - 10
DO - http://doi.org/
UR - https://global-sci.org/intro/article_detail/jics/22562.html
KW - face recognition, Shearlets features, local binary pattern, BFLD, PCA, feature integration
AB - 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.
ZhiyongZeng. (2024). Fusing Shearlets and LBP Feature Sets for Face Recognition.
Journal of Information and Computing Science. 10 (1).
029-039.
doi:
Copy to clipboard