Abstract—Linear discrimination analysis (LDA) technique is an important and well-developed area of image recognition and to date many linear discrimination methods have been put forward. Despite these efforts, there persist in LDA at least three areas of weakness. The first weakness is that not all the discrimination vec-tors that are obtained are useful in pattern classification. Second, it remains computationally expensive to make the discrimination vectors completely satisfy statistical uncorrelation. The third weakness is that it is necessary to select the appropriate principal components. In this paper, we propose to improve discrimination technique in these three areas and to that end present an improved LDA (ILDA) approach which synt...
Abstract. Recently in face recognition, as opposed to our expectation, the performance of an ICA (In...
Subspace methods such as Linear Discriminant Analysis (LDA) are efficient in dimension reduction and...
In the last decade, many variants of classical linear discriminant analysis (LDA) have been develope...
In this dissertation, we investigate the pattern recognition performance of Additive Linear Discrimi...
Linear Discriminant Analysis (LDA) has been successfully applied to face recognition which is based ...
A critical issue of applying Linear Discriminant Analysis (LDA) is both the singularity and instabil...
Abstract: In this paper two Face Recognition techniques, Principal Component Analysis (PCA) and Line...
Linear discriminant analysis (LDA) has been an active topic of research during the last century. How...
In this paper, new improvements for the linear discrimination technique are proposed. These improvem...
International Conference on Biometrics, ICB 2006, Hong Kong, 5-7 January 2006The Fisherface method i...
Face recognition is used in wide range of application. In recent years, face recognition has become ...
Abstract—High-dimensional data are common in many do-mains, and dimensionality reduction is the key ...
In this paper, we focus on face recognition over image sets, where each set is represented by a line...
In this paper we describe a holistic face recognition method based on subspace Linear Discriminant A...
Face recognition system should be able to automatically detect a face in images. This involves extra...
Abstract. Recently in face recognition, as opposed to our expectation, the performance of an ICA (In...
Subspace methods such as Linear Discriminant Analysis (LDA) are efficient in dimension reduction and...
In the last decade, many variants of classical linear discriminant analysis (LDA) have been develope...
In this dissertation, we investigate the pattern recognition performance of Additive Linear Discrimi...
Linear Discriminant Analysis (LDA) has been successfully applied to face recognition which is based ...
A critical issue of applying Linear Discriminant Analysis (LDA) is both the singularity and instabil...
Abstract: In this paper two Face Recognition techniques, Principal Component Analysis (PCA) and Line...
Linear discriminant analysis (LDA) has been an active topic of research during the last century. How...
In this paper, new improvements for the linear discrimination technique are proposed. These improvem...
International Conference on Biometrics, ICB 2006, Hong Kong, 5-7 January 2006The Fisherface method i...
Face recognition is used in wide range of application. In recent years, face recognition has become ...
Abstract—High-dimensional data are common in many do-mains, and dimensionality reduction is the key ...
In this paper, we focus on face recognition over image sets, where each set is represented by a line...
In this paper we describe a holistic face recognition method based on subspace Linear Discriminant A...
Face recognition system should be able to automatically detect a face in images. This involves extra...
Abstract. Recently in face recognition, as opposed to our expectation, the performance of an ICA (In...
Subspace methods such as Linear Discriminant Analysis (LDA) are efficient in dimension reduction and...
In the last decade, many variants of classical linear discriminant analysis (LDA) have been develope...