Over the past few decades, interest in theories and algorithms for face recognition has been
growing rapidly. Video surveillance, criminal identification, building access control, and …

Biometric systems have the goal of measuring and analyzing the unique physical or
behavioral characteristics of an individual. The main feature of biometric systems is the use …

Electronics industry is one of the fastest evolving, innovative, and most competitive
industries. In order to meet the high consumption demands on electronics components …

We present a nonlinear version of the well-known anomaly detection method referred to as
the RX-algorithm. Extending this algorithm to a feature space associated with the original …

This tutorial explains Linear Discriminant Analysis (LDA) and Quadratic Discriminant
Analysis (QDA) as two fundamental classification methods in statistical and probabilistic …

Dimensionality reduction is an important aspect in the pattern classification literature, and
linear discriminant analysis (LDA) is one of the most widely studied dimensionality reduction …

This paper examines the theory of kernel Fisher discriminant analysis (KFD) in a Hilbert
space and develops a two-phase KFD framework, ie, kernel principal component analysis …

Increasingly large amount of multidimensional data are being generated on a daily basis in
many applications. This leads to a strong demand for learning algorithms to extract useful …

One of the main challenges faced by the current face recognition techniques lies in the
difficulties of collecting samples. Fewer samples per person mean less laborious effort for …

Heterogeneous face recognition (HFR) involves matching two face images from alternate
imaging modalities, such as an infrared image to a photograph or a sketch to a photograph …