J
Juwei Lu
Researcher at University of Toronto
Publications - 30
Citations - 3804
Juwei Lu is an academic researcher from University of Toronto. The author has contributed to research in topics: Facial recognition system & Linear discriminant analysis. The author has an hindex of 17, co-authored 29 publications receiving 3662 citations. Previous affiliations of Juwei Lu include Nanyang Technological University.
Papers
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Journal ArticleDOI
Face recognition using LDA-based algorithms
TL;DR: A new algorithm is proposed that deals with both of the shortcomings in an efficient and cost effective manner of traditional linear discriminant analysis methods for face recognition systems.
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Face recognition with radial basis function (RBF) neural networks
TL;DR: A novel paradigm is proposed whereby data information is encapsulated in determining the structure and initial parameters of the RBF neural classifier before learning takes place, and the dimension of the search space is drastically reduced in the gradient paradigm.
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Face recognition using kernel direct discriminant analysis algorithms
TL;DR: This paper proposes a kernel machine-based discriminant analysis method, which deals with the nonlinearity of the face patterns' distribution and effectively solves the so-called "small sample size" (SSS) problem, which exists in most FR tasks.
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Face recognition using the nearest feature line method
Stan Z. Li,Juwei Lu +1 more
TL;DR: A novel classification method, called the nearest feature line (NFL), for face recognition, based on the nearest distance from the query feature point to each FL, which achieves the lowest error rate reported for the ORL face database.
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Regularization studies of linear discriminant analysis in small sample size scenarios with application to face recognition
TL;DR: A new LDA method is proposed that attempts to address the SSS problem using a regularized Fisher's separability criterion and a scheme of expanding the representational capacity of face database is introduced to overcome the limitation that the LDA-based algorithms require at least two samples per class available for learning.