The field of measurement technology in the sensors domain is rapidly changing due to the
availability of statistical tools to handle many variables simultaneously. The phenomenon …

Principal Component Analysis (PCA) is the best face recognition method. This research
suggests PCA for fingerprint and signature recognition. Simple image processing transforms …

Data fusion can be performed at different levels, eg, pixel, feature, score, and decision.
Accordingly, the corresponding data preprocessing is also different for each level. For …

In a multimodal biometric system, the effective fusion method is necessary for combining
information from various single modality systems. In this paper the performance of sum rule …

Canonical correlation analysis (CCA) and partial least squares (PLS) are always used as
fusing two feature sets. How to extend them to fuse multiple features in a generalized way is …

This textbook provides a comprehensive introduction to the concepts and idea of
multisensor data fusion. It is an extensively revised second edition of the author's successful …

Abstract Modular Principal Component Analysis (ModPCA) divides a pattern into sub-
patterns and extracts local Principal Components (PCs) from the sub-patterns. It is aimed to …

Unimodal biometric systems are susceptible to a variety of problems such as noisy data,
intra-class variations, limited degrees of freedom, non-universality, spoof attacks and …

Biometrics is now being principally employed in many daily applications ranging from the
border crossing to mobile user authentication. In the high‐security scenarios, biometrics …

In this paper, we present Discriminant Correlation Analysis (DCA), a feature level fusion
technique that incorporates the class associations in correlation analysis of the feature sets …