Biometric Authentication: A Machine Learning Approach

2.5. Visual-Based Feature Extraction and Pattern Classification

This section discusses several commonly used feature extraction algorithms for visual-based biometric systems. The most important task of a feature extractor is to extract the discriminant information that is invariant to as many variations embedded in the raw data (e.g., scaling, translation, rotation) as possible. Since various biometric methods have different invariant properties (e.g., minutiae positions in fingerprint methods, texture patterns in iris approaches), it is important for the system designer to understand the natural characteristics of the biometric signals and the noise models that could possibly be embedded in the data acquisition process.

Table 2.3 shows several ...