In this paper, a person is identified with face as a biometric feature using modified fisher face and fuzzy fisher face. The premise of this paper is to introduce modified fisher face, fuzzy fisher face and include gradual level of assignment to class being regarded as a membership grade which helps to improve recognition results. Performance of the said system is compared with traditional fish...

In this paper, a framework of Discriminant Low-dimensional Subspace Analysis (DLSA) method is proposed to deal with the Small Sample Size (SSS) problem in face recognition area. Firstly, it is rigorously proven that the null space of the total covariance matrix, St , is useless for recognition. Therefore, a framework of Fisher discriminant analysis in a low-dimensional space is developed by pro...

In this paper, a framework of Discriminant Subspace Analysis (DSA) method is proposed to deal with the Small Sample Size (SSS) problem in face recognition area. Firstly, it is rigorously proven that the null space of the total covariance matrix, St, is useless for recognition. Therefore, a framework of Fisher discriminant analysis in a low-dimensional space is developed by projecting all the sa...

Dimensionality reduction is one of the important preprocessing steps in practical pattern recognition. SEmi-supervised Local Fisher discriminant analysis (SELF)— which is a semi-supervised and local extension of Fisher discriminant analysis—was shown to work excellently in experiments. However, when data dimensionality is very high, a naive use of SELF is prohibitive due to high computational c...

Linear discriminant analysis introduced by Fisher is a known dimension reduction and classification approach that has received much attention in the statistical literature. Most researchers have focused attention on its deficiencies. As such different versions of classification procedures have been introduced for various applications. In this paper, we attempt not to robustify the Fisher linear...

Mika et al. [1] apply the “kernel trick” to obtain a non-linear variant of Fisher’s linear discriminant analysis method, demonstrating state-of-the-art performance on a range of benchmark datasets. We show that leave-one-out cross-validation of kernel Fisher discriminant classifiers can be implemented with a computational complexity of only O(l3) operations rather than the O(l4) of a näıve impl...

ÐWe derive a class of computationally inexpensive linear dimension reduction criteria by introducing a weighted variant of the well-known K-class Fisher criterion associated with linear discriminant analysis (LDA). It can be seen that LDA weights contributions of individual class pairs according to the Euclidian distance of the respective class means. We generalize upon LDA by introducing a dif...

Aurora is the typical ionosphere track generated by the interaction of solar wind and magnetosphere. This paper proposed a method based on eigenvalue-scaling kernel fisher discriminant analysis and Karhunen-Loeve Transform (KLT) to take advantage of interband correlation between aurora images to detect the change of aurora in serial time. Conventional classification algorithms are incapable of ...

Thai sentences can be simplified or shortened by simply cutting some words out without changing its meaning. In this paper, Linear and non-linear Fisher discriminant analysis are applied to shorten Thai paragraph in a corpus. Features used in this paper are unique word ID and part of speech of the target word, as well as its three previous and three next adjacent words, and also its role as con...

A novel model for Fisher discriminant analysis is developed in this paper. In the new model, maximal Fisher criterion values of discriminant vectors and minimal statistical correlation between feature components extracted by discriminant vectors are simultaneously required. Then the model is transformed into an extreme value problem, in the form of an evaluation function. Based on the evaluatio...