The goal of any clever processing is to reduce
overhead associated with the theater computations while at the same time to capitalize
on an output. The same principle governs performance of most public and profitable
organizations—to accomplish high manufacture by resource and processes
optimization. While appearance-based methods excel in capturing even subtle
features in the massive amount of high-dimensional data, sometimes generalizing
the results and noting common patterns leads to process optimization without
sacrificing the security system presentation. This section presents
topology-based methods, which work best with biometric data that has well-known
geometric features, such as fingerprint or hand/palm biometrics.
Identifying patterns in behavioral biometrics, in general, is a to some
extent different and somewhat more complex problem than identifying features in
physiological biometrics. Examples of behavioral biometrics include signature,
voice, gait, and typing patterns. Due to chronological dynamic features
associated with each biometric (samples must be pragmatic over period of time
for best matching results), these problems are often treated in a class of
signal-processing methods. In a nutshell, the task and the overall biometric
system architecture remain the same, however upon closer examination; some very
specialized methods taking advantage of unique continuous nature of those biometrics
have been developed.
In this chapter, different image processing methods and algorithms that
are popular in biometric data processing has been presented. In the case of the
most of the biometric identifiers used today, image of that identifier is mainly
the input to the biometric system. Thus, the processing of the biometric images
is very essential for efficient and reliable performance of the biometric
system.
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