Due to the robustness of Gabor features against local distortions caused by variance of
illumination, expression and pose, they have been successfully applied for face recognition …

Dimensionality reduction is an important aspect in the pattern classification literature, and
linear discriminant analysis (LDA) is one of the most widely studied dimensionality reduction …

Linear Discriminant Analysis (LDA) is a very common technique for dimensionality reduction
problems as a preprocessing step for machine learning and pattern classification …

Statistical pattern recognition is a very active area of study andresearch, which has seen
many advances in recent years. New andemerging applications-such as data mining, web …

A complete introduction to discriminant analysis--extensively revised, expanded, and
updated This Second Edition of the classic book, Applied Discriminant Analysis, reflects and …

Along with water, gas, electricity, and telephone, cloud computing has been considered as
the fifth utility. Like other utility services available in today's social computing services are …

Common spatial pattern (CSP) is a popular algorithm for classifying electroencephalogram
(EEG) signals in the context of brain-computer interfaces (BCIs). This paper presents a …

A novel and uniform framework for both face identification and verification is presented in
this paper. The framework is based on a combination of Gabor wavelets and General …

This work proposes a subspace approach that regularizes and extracts eigenfeatures from
the face image. Eigenspace of the within-class scatter matrix is decomposed into three …

It is well-known that the applicability of linear discriminant analysis (LDA) to high-
dimensional pattern classification tasks such as face recognition often suffers from the so …