Local Similarity based Discriminant Analysis for Face Recognition
Fisher linear discriminant analysis (LDA) is one of the most popular projection techniques for
feature extraction and has been widely applied in face recognition. However, it cannot be …
feature extraction and has been widely applied in face recognition. However, it cannot be …
Local similarity based linear discriminant analysis for face recognition with single sample per person
F Liu, Y Bi, Y Cui, Z Tang - … : Singapore, Singapore, November 1-2, 2014 …, 2015 - Springer
Fisher linear discriminant analysis (LDA) is one of the most popular projection techniques for
feature extraction and has been widely applied in face recognition. However, it cannot be …
feature extraction and has been widely applied in face recognition. However, it cannot be …
[PDF][PDF] Maximal Margin Local Preserving Median Fisher Discriminant Analysis for Face Recognition.
X Liang, Yu'e Lin 0001 - J. Softw., 2016 - jsoftware.us
Median Fisher Discriminator (MFD) used the class median vector is more effective than
Linear discriminant analysis (LDA). However, MFD only captures global geometrical …
Linear discriminant analysis (LDA). However, MFD only captures global geometrical …
A spatial regularization of LDA for face recognition
LJ Park - International Journal of Fuzzy Logic and Intelligent …, 2010 - koreascience.kr
This paper proposes a new spatial regularization of Fisher linear discriminant analysis
(LDA) to reduce the overfitting due to small size sample (SSS) problem in face recognition …
(LDA) to reduce the overfitting due to small size sample (SSS) problem in face recognition …
An improved linear discriminant analysis method and its application to face recognition
K Li, P Tang - Applied Mechanics and Materials, 2014 - Trans Tech Publ
Linear discriminant analysis (LDA) is an important feature extraction method. This paper
proposes an improved linear discriminant analysis method, which redefines the within-class …
proposes an improved linear discriminant analysis method, which redefines the within-class …
Optimal subspace analysis for face recognition
Fisher Linear Discriminant Analysis (LDA) has been successfully used as a data
discriminantion technique for face recognition. This paper has developed a novel subspace …
discriminantion technique for face recognition. This paper has developed a novel subspace …
Parameter-free marginal fisher analysis based on L2,1-norm regularisation for face recognition
Y Lin, Z Ren, X Liang, S Zhang - International Journal of …, 2023 - inderscienceonline.com
Marginal fisher analysis is an effective feature extraction algorithm for face recognition, but
the algorithm is sensitive to the influence of the neighbourhood parameter setting, and does …
the algorithm is sensitive to the influence of the neighbourhood parameter setting, and does …
Regularized locality preserving discriminant analysis for face recognition
X Gu, W Gong, L Yang - Neurocomputing, 2011 - Elsevier
This paper proposes a regularized locality preserving discriminant analysis (RLPDA)
approach for facial feature extraction and recognition. The RLPDA approach decomposes …
approach for facial feature extraction and recognition. The RLPDA approach decomposes …
A new LDA-based face recognition system which can solve the small sample size problem
A new LDA-based face recognition system is presented in this paper. Linear discriminant
analysis (LDA) is one of the most popular linear projection techniques for feature extraction …
analysis (LDA) is one of the most popular linear projection techniques for feature extraction …
Face recognition using enhanced linear discriminant analysis
H Hu, P Zhang, F De la Torre - IET computer vision, 2010 - IET
There are two fundamental problems with the linear discriminant analysis (LDA) for face
recognition. First one is LDA is not stable because of the small training sample size problem …
recognition. First one is LDA is not stable because of the small training sample size problem …