Providing a broad but in-depth introduction to neural network and machine learning in a
statistical framework, this book provides a single, comprehensive resource for study and …

Primarily motivated by some characteristics of the human visual cortex (HVC), we propose a
new facial expression recognition scheme, involving a statistical synthesis of hierarchical …

In contrast to holistic methods, local matching methods extract facial features from different
levels of locality and quantify them precisely. To determine how they can be best used for …

Fisher linear discriminant analysis (LDA) is a classical subspace learning technique of
extracting discriminative features for pattern recognition problems. The formulation of the …

Face stands out as a preferable biometric trait for automatic human authentication as it is
intuitive and non-intrusive. This paper investigates various feature-based automatic face …

In this paper, a novel binary classifier coined projection twin support vector machine
(PTSVM) is proposed. The idea is to seek two projection directions, one for each class, such …

In this paper we formulate a least squares version of the recently proposed projection twin
support vector machine (PTSVM) for binary classification. This formulation leads to …

For the recently proposed projection twin support vector machine (PTSVM)[1], we propose a
simpler and reasonable variant from theoretical point of view, called projection twin support …

In this paper, we describe a classification framework for binary shapes that have scale,
rotation and strong viewpoint variations. To this end, we develop several novel techniques …

Recognizing human faces in various lighting conditions is quite a difficult problem. The
problem becomes more difficult when face images are taken in extremely high dynamic …