Add to ORKGThe problem of determining the optimal set of discriminant vectors for feature extraction in pattern recognition is investigated. We propose a new direct LDA (D-LDA) method that is applicable to small sample size (SSS) problems often arising in face recognition. The experimental results on two popular databases show the effectiveness of the proposed method
Low-dimensional feature representation with enhanced discriminatory power of paramount importance to...
This study investigates a new method of feature extraction for classification prob-lems. The method ...
Singularity problem in human face feature extraction is very challenging that has gained a lot of at...
The problem of determining the optimal set of discriminant vectors for feature extraction in pattern...
Linear Discriminant Analysis (LDA) has been successfully used as a dimensionality reduction techniqu
doi:10.4156/jdcta.vol4. issue9.29 The dimensionality of sample is often larger than the number of tr...
Abstract – In face recognition, LDA often encounters the so-called small sample size (SSS) problem, ...
Face recognition system should be able to automatically detect a face in images. This involves extra...
Feature extraction is one of important process in face recognition LDA is dimensional reduction tech...
Linear Discriminant Analysis (LDA) is a popular feature extraction technique that aims at creating a...
A critical issue of applying Linear Discriminant Analysis (LDA) is both the singularity and instabil...
Linear discriminant analysis (LDA) is a basic tool of pattern recognition, and it is used in extensi...
Linear Discriminant Analysis (LDA) has been successfully applied to face recognition which is based ...
This paper develops a new image feature extraction and recognition method coined two-dimensional lin...
Abstract-- In this paper, we present a face recognition system that identifies a person from the inp...
Low-dimensional feature representation with enhanced discriminatory power of paramount importance to...
This study investigates a new method of feature extraction for classification prob-lems. The method ...
Singularity problem in human face feature extraction is very challenging that has gained a lot of at...
The problem of determining the optimal set of discriminant vectors for feature extraction in pattern...
Linear Discriminant Analysis (LDA) has been successfully used as a dimensionality reduction techniqu
doi:10.4156/jdcta.vol4. issue9.29 The dimensionality of sample is often larger than the number of tr...
Abstract – In face recognition, LDA often encounters the so-called small sample size (SSS) problem, ...
Face recognition system should be able to automatically detect a face in images. This involves extra...
Feature extraction is one of important process in face recognition LDA is dimensional reduction tech...
Linear Discriminant Analysis (LDA) is a popular feature extraction technique that aims at creating a...
A critical issue of applying Linear Discriminant Analysis (LDA) is both the singularity and instabil...
Linear discriminant analysis (LDA) is a basic tool of pattern recognition, and it is used in extensi...
Linear Discriminant Analysis (LDA) has been successfully applied to face recognition which is based ...
This paper develops a new image feature extraction and recognition method coined two-dimensional lin...
Abstract-- In this paper, we present a face recognition system that identifies a person from the inp...
Low-dimensional feature representation with enhanced discriminatory power of paramount importance to...
This study investigates a new method of feature extraction for classification prob-lems. The method ...
Singularity problem in human face feature extraction is very challenging that has gained a lot of at...