To address two problems, namely nonlinear problem and singularity problem, of linear
discriminant analysis (LDA) approach in face recognition, this paper proposes a novel …

This paper addresses two problems in linear discriminant analysis (LDA) of face recognition.
The first one is the problem of recognition of human faces under pose and illumination …

Kernel discriminant analysis (KDA) is a widely used tool in feature extraction community.
However, for high-dimensional multi-class tasks such as face recognition, traditional KDA …

In the last decade, many variants of classical linear discriminant analysis (LDA) have been
developed to tackle the under-sampled problem in face recognition. However, choosing the …

Marginal fisher analysis is an effective feature extraction algorithm for face recognition, but
the algorithm is sensitive to the influence of the neighbourhood parameter setting, and does …

In this paper, a regularized kernel locality preserving discriminant analysis (RKLPDA)
method is proposed for facial feature extraction and recognition. The proposed RKLPDA …

There are two fundamental problems with the linear discriminant analysis (LDA) for face
recognition. First one is LDA is not stable because of the small training sample size problem …

In face recognition tasks, Fisher discriminant analysis (FDA) is one of the promising methods
for dimensionality reduction and discriminant feature extraction. The objective of FDA is to …

In this paper, we propose a new kernel discriminant analysis called kernel relevance
weighted discriminant analysis (KRWDA) which has several interesting characteristics. First …

This paper proposes a new image feature extraction method for face recognition, called dual-
kernel based two dimensional linear discriminant analysis (D-K2DLDA), by integrating …