Face recognition has become more significant and relevant in recent years owing to it
potential applications. Since the faces are highly dynamic and pose more issues and …

Subspace learning methods are very sensitive to the illumination, translation, and rotation
variances in image recognition. Thus, they have not obtained promising performance for …

A sparse representation-based classifier (SRC) is developed and shows great potential for
real-world face recognition. This paper presents a dimensionality reduction method that fits …

Factors such as misalignment, pose variation, and occlusion make robust face recognition a
difficult problem. It is known that statistical features such as local binary pattern are effective …

A new graph based constrained semi-supervised learning (G-CSSL) framework is proposed.
Pairwise constraints (PC) are used to specify the types (intra-or inter-class) of points with …

A novel discriminant analysis criterion is derived in this paper under the theoretical
framework of Bayes optimality. In contrast to the conventional Fisher's discriminant criterion …

Local discriminant embedding (LDE) has been recently proposed to overcome some
limitations of the global linear discriminant analysis method. In the case of a small training …

This paper presents a novel and uniform framework for face recognition. This framework is
based on a combination of Gabor wavelets, direct linear discriminant analysis (DLDA) and …

In this paper, the use of multiple variable spaces is proposed for monitoring modern
industrial processes where data for a large number of process variables may be collected …

In kernel methods such as kernel principal component analysis (PCA) and support vector
machines, the so called kernel trick is used to avoid direct calculations in a high (virtually …