Non-minutiae based fingerprint descriptor

Abstract

Fingerprint recognition refers to the techniques of identifying or verifying a match between human fingerprints. Fingerprint recognition has been one of the hot research areas in recent years, and it plays an important role in personal identification (Maio et al., 2003). A general fingerprint recognition system consists of some important steps, such as fingerprint preprocessing, feature extraction, matching, and so on. Usually, a descriptor is defined to identify an item with information storage. A fingerprint descriptor is used to descript and represent a fingerprint image for personal identification. Various fingerprint descriptors have been proposed in the literature. Two main categories for fingerprint descriptors can be classified into minutiae based and non-minutiae based. Minutiae based descriptors (Jain et al. 1997a; Jain et al. 1997b; Liu et al. 2000; Ratha et al. 1996; He et al. 2007; Cappelli et al. 2011) are the most popular algorithms for fingerprint recognition and are sophisticatedly used in fingerprint recognition systems. The major minutia features of fingerprint ridges are: ridge ending, ridge bifurcation and so on (Maio et al., 2003). Minutiae based descriptor use a feature vector extracted from fingerprints as sets of points in a multi-dimensional space, which comprise several characteristics of minutiae such as type, position, orientation, etc. The matching is to essentially search for the best alignment between the template and the input minutiae sets. However, due to the poor image quality and complex input conditions, minutiae are not easy to be accurately determined, thus it may result in low matching accuracy. In addition, minutiae based descriptors may not fully utilize the rich discriminatory information available in the fingerprints with high computational complexity. Non-minutiae based descriptors (Amornraksa & Tachaphetpiboon, 2006; Benhammadi, et al. 2007;Jain et al.,2000; Jin et al., 2004; Nanni and Lumini, 2008; Nanni & Lumini, 2009; Ross, et al. 2003; Sha et al. 2003;Tico et al. 2001; Yang et al., 2006; Yang & Park, 2008a; Yang & Park, 2008b), however, overcome the demerits of the minutiae based method. It uses features other than characteristics of minutiae from the fingerprint ridge pattern, such as local orientation and frequency, ridge shape, and texture information. It can extract more rich discriminatory information and abandon the pre-processing process such as binarization and thinning and post minutiae processing. Other merits are listed by using the nonminutiae based methods, such as a high accuracy; a fast processing speed; a fixed length feature vector; easily coupled with other system; being combined with Biohashing and so on.