INTELLIGENT PATTERN ANALYSIS IN BIOMETRICS

The goal of any intelligent processing is to minimize overhead associated with performing computations while at the same time to maximize an output. The same principle governs behavior of most public and commercial organizations—to achieve high production by resource and processes optimization. While appearance-based methods excel in capturing even subtle features in the multitude of high-dimensional data, sometimes generalizing the results and noting common patterns leads to process optimization without sacrificing the security system performance. This section presents topology-based methods, which work best with biometric data that has prominent geometric features, such as fingerprint or hand/palm biometrics. We start by outlining the topology-based methodology with the roots in computational geometry.

Identifying patterns in behavioral biometrics, in general, is a slightly 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 temporal dynamic features associated with each biometric (samples must be observed 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 that biometrics have been developed.

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. Usually, the main methods which are used for biometric image processing are digitization, compression, enhancement, segmentation, feature measurement, image representation, image models and design methodology. The feature extraction methods have been classified as appearance-based and topological feature-based, and illustrated on example of different fingerprint recognition.