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» Block LDA for Face Recognition
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ICMCS
2006
IEEE
160views Multimedia» more  ICMCS 2006»
14 years 3 months ago
Selecting Kernel Eigenfaces for Face Recognition with One Training Sample Per Subject
It is well-known that supervised learning techniques such as linear discriminant analysis (LDA) often suffer from the so called small sample size problem when apply to solve face ...
Jie Wang, Konstantinos N. Plataniotis, Anastasios ...
ICPR
2004
IEEE
14 years 11 months ago
Information Fusion in Face Identification
Information Fusion of multi-modal Biometrics has attracted much attention in recent years. However, this paper focuses on the information fusion in single modals, that is, the fac...
Bo Cao, Peng Yang, Shiguang Shan, Wen Gao, Wenchao...
NIPS
2007
13 years 11 months ago
Subspace-Based Face Recognition in Analog VLSI
We describe an analog-VLSI neural network for face recognition based on subspace methods. The system uses a dimensionality-reduction network whose coefficients can be either progr...
Gonzalo Carvajal, Waldo Valenzuela, Miguel Figuero...
ICB
2007
Springer
308views Biometrics» more  ICB 2007»
14 years 4 months ago
Learning Multi-scale Block Local Binary Patterns for Face Recognition
Abstract. In this paper, we propose a novel representation, called Multiscale Block Local Binary Pattern (MB-LBP), and apply it to face recognition. The Local Binary Pattern (LBP) ...
ShengCai Liao, XiangXin Zhu, Zhen Lei, Lun Zhang, ...
NIPS
2004
13 years 11 months ago
Efficient Kernel Discriminant Analysis via QR Decomposition
Linear Discriminant Analysis (LDA) is a well-known method for feature extraction and dimension reduction. It has been used widely in many applications such as face recognition. Re...
Tao Xiong, Jieping Ye, Qi Li, Ravi Janardan, Vladi...