The epidermal layer of the distal phalanx of a nger is covered with concentric raised friction ridges (Digital Persona, 2004). As well as helping us to grip objects, these ridges provide distinct characteristics or features. The ridges are formally known as papillary ridges because minuscule perspiration pores are prolic along them (Inbau, 1934). For the purpose of recognition, ngerprint features can be broadly classied into two categories: global and local.
4.3.1 Global Features
Global features are those ngerprint characteristics that are visible to the naked eye (Digital Persona, 2004). The following is a description of global ngerprint features, some of which are illustrated in Figure 4.2:
• Pattern area: the region of the ngerprint where ridge lines form highly dis- tinguishable shapes or patterns and are clearly apparent. Ridge lines in these regions tend to exhibit high curvature.
• Basic ridge pattern: the discernible patterns made by ridge lines that have been dened into categories or classes. They are located within the pattern area, and are broadly classied as: arch, loop, and whorl. These basic ridge patterns are described in section 4.4.5.
• Core point: the upper most point (in relation to the tip of the nger) of the inner most ridge line (Henry, 1900). Core points are typically (though not always) located near the centre of the pattern area. Figure 4.2 provides an example of a core point. Core points (if present and determinable) can be used as a reference point for determining class (refer section 4.4.5), to assist in ngerprint image alignment, and also to facilitate ridge counting.
• A delta point may be formed in two ways (Henry, 1900):
1. When a single ridge abruptly bifurcates into two, and the two diverging ridges depart in opposite directions (refer Figure 4.2 for an example).
2. When two ridges that had previously been running side by side abruptly diverge into opposite directions.
Along with the core point, a delta point (if present and determinable) can be used as a reference point for determining class (refer section 4.4.5), to assist in ngerprint image alignment, and also to facilitate ridge counting.
Figure 4.2: Fingerprint Impression Illustrating Core And Delta Points • Ridge count: the number of ridges crossing the imaginary line segment between
a core point and a delta point.
• Minutiae count: the total number of minutia points (refer section 4.3.2). Because global features are more easily detectable than local features, they are commonly used to classify ngerprints into general categories or classes (Wayman et al., 2005). These categories are based on the basic ridge patterns (section 4.4.5). Categorising ngerprints allows for ecient identication, within large record sys- tems, during the validation phase. Once the identication process narrows the search space to one category, verication becomes a more manageable task. Importantly, global features are insuciently distinctive enough for the purpose of verication.
4.3.2 Local Features
Local features dier from global features in that they are not visible to the naked eye. Fingerprint ridges are not continuous straight lines; they may break, fork, change direction, or terminate (Digital Persona, 2004). The point of discontinuity is called a minutia point (the more common plural term being minutiae) (Galton, 1892).
There are ve characteristics of a minutia point (Digital Persona, 2004):
1. Type: there are really only two primary types (Yager and Amin, 2004b). However, variations of the two primary types occur, as described below and illustrated in Figure 4.3:
• Ridge termination is when a ridge ends abruptly. Two variations are: Independent ridge: a short ridge terminating at both ends. Dot or Island: a very small ridge that appears to be a dot.
• Ridge bifurcation (branching or forking) is when a ridge divides into two or more individual ridges. A variation is:
Enclosure: a ridge that divides into two and then reunites to create an enclosed area. The length of the enclosure is typically quite small, with the ridges reuniting shortly after diverging.
2. Position: the location of the minutia point, determined as x, y coordinates in a two dimensional coordinate system. Figure 4.4 provides an example of minutiae whose positions are registered in a coordinate system; grid lines on the x axis are spaced 40 units apart and grid lines on the y axis are spaced 50 units apart. Each minutia point is identied by a red circle.
3. Spatial frequency: the average distance between ridges in the neighbourhood of a minutia.
Figure 4.3: Local Fingerprint Features Types
4. Orientation: the angle between the tangent to the ridge at a minutia position and the horizontal axis (i.e. the axis at right angles to the vertical axis of the nger). Note that the ridge used for calculating the orientation is determined according to the minutia typetermination or bifurcation (Maltoni et al., 2003):
• For a terminating minutia, the orientation is determined by the ridge that approaches the point.
• For a bifurcating minutia, the orientation is determined by the centre line of the furrow that approaches the point.
In Figure 4.4, the red tail extending from a red circle (which highlights a minutia point), indicates the direction of the tangent which provides the ori- entation.
5. Curvature: the rate of change of the ridge orientation as the ridge approaches a minutia. As just described in point 4 above, the ridge used for calculation is determined according to the minutia type.
Figure 4.4: Local Features Illustrating Minutiae Positions
In automated systems, considerable diculties have been encountered when dis- tinguishing between the minutia types listed above (Yager and Amin, 2004b). Usu- ally, to limit uncertainty, only the primary types are dierentiated (i.e. ridge termi- nation and ridge bifurcation).
These local features of ngerprint ridges are the unique characteristics used for verication during the validation phase. It is possible for two or more individuals to have almost identical global features but still be dierentiated by their local features. Therefore, during the validation phase, global features are better suited to identication, whereas local features are necessary for verication.
Typically, all ngers have a dierent number of minutiae. Even if two ngers have the same number of minutiae, they will be in dierent relative positions. This relative positioning of minutiae forms a unique conguration or pattern. It is this pattern and the other minutiae characteristics that are used in the verication pro- cess (Maltoni et al., 2003).
Previous studies have shown that successive ngerprint scans of the same n- ger produce images that almost never match perfectly (Digital Persona, 2004).
That is, two dierent prints of the same nger will rarely be identical. Reasons for this situation could be sensor inaccuracy (resulting in missing data or introduced ar- tifacts), positional variation due to instrument noise, imperfect imaging conditions, changes in physiological characteristics, ambient conditions, and the elasticity of the epidermal layer of the nger (Maltoni et al., 2003). However, the distinctive feature patterns will still be evident.