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Spurious Fingerprint Minutiae Detection Based on Multiscale Directional Information

Reliable and accurate automatic personal authentication is becoming a necessity for operation of modern society. Biometrics, as rapidly evolving technology which identifies people based on their physiological or behavioral characteristics, is becoming dominant over traditional means of authentication such as knowledgebased (something you know – password) and token-based (something you have – key) authentication. Biometrics has been widely used in forensics, and has the potential to be widely adopted in a very broad range of civil applications (ID cards, border control, physical access control, computer data security...). In another words, biometrics is becoming a necessary component of any ID management system. Amongst available biometric characteristics fingerprints are, due to their characteristics, one of the most researched, used and mature method of authentication [1, 2]. A fingerprint represents the image of the surface of the skin of a fingertip. A typical structure of a fingerprint consists of ridges and valleys. The ridge pattern in a fingerprint can be described as an oriented texture pattern with fixed dominant spatial frequency and orientation in a local neighborhood. The frequency is depending on inter-ridge spacing, and orientation on flow pattern exhibited by the ridges. Region of a fingerprint where the ridge pattern makes it visually prominent are called singularities [3]. There are two types of fingerprint singularities: core and delta, and they are very useful for determining fingerprint’s class. A closer analysis of the fingerprint reveals some anomalies of the ridges, such as ridge endings, bifurcations, crossovers, short ridges, etc. These local features of fingerprints, called minutiae, can be used for manual or automatic fingerprint identification since their number and position defines fingerprint’s individuality. The American National Standards Institute has proposed a classification of minutiae into four main groups: endings (terminations), bifurcations, crossovers and undetermined [4]. The most important ones are ending and bifurcation. Their discriminatory capability is very high. As shown in [5] the probability a fingerprint image containing 36 minutiae will match 12 minutiae with a different fingerprint also containing 36 minutiae is 6,1×10 (i.e. is unique among 16 million fingerprints based on 12 corresponding minutiae pairs). Typical structure and basic features of fingerprints (singularities and minutiae) are shown in Fig. 1.