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» Boosting linear discriminant analysis for face recognition
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ICCV
2001
IEEE
14 years 9 months ago
Separating Appearance from Deformation
By representing images and image prototypes by linear subspaces spanned by "tangent vectors" (derivatives of an image with respect to translation, rotation, etc.), impre...
Nebojsa Jojic, Patrice Simard, Brendan J. Frey, Da...
MMM
2007
Springer
143views Multimedia» more  MMM 2007»
14 years 2 months ago
Semi-supervised Cast Indexing for Feature-Length Films
Abstract. Cast indexing is a very important application for contentbased video browsing and retrieval, since the characters in feature-length films and TV series are always the ma...
Wei Fan, Tao Wang, Jean-Yves Bouguet, Wei Hu, Yimi...
ACCV
2010
Springer
13 years 2 months ago
Randomised Manifold Forests for Principal Angle-Based Face Recognition
Abstract. In set-based face recognition, each set of face images is often represented as a linear/nonlinear manifold and the Principal Angles (PA) or Kernel PAs are exploited to me...
Ujwal D. Bonde, Tae-Kyun Kim, K. R. Ramakrishnan
ICIP
2002
IEEE
14 years 9 months ago
Face recognition using mixtures of principal components
We introduce an efficient statistical modeling technique called Mixture of Principal Components (MPC). This model is a linear extension to the traditional Principal Component Anal...
Deepak S. Turaga, Tsuhan Chen
TKDE
2011
479views more  TKDE 2011»
13 years 2 months ago
Learning Semi-Riemannian Metrics for Semisupervised Feature Extraction
—Discriminant feature extraction plays a central role in pattern recognition and classification. Linear Discriminant Analysis (LDA) is a traditional algorithm for supervised feat...
Wei Zhang, Zhouchen Lin, Xiaoou Tang