Implementation of Minutiae Based Fingerprint Identification System Using Crossing Number Concept

By Chaudhari, Atul S.; Patnaik, Girish K. et al. | Informatica Economica, January 1, 2014 | Go to article overview

Implementation of Minutiae Based Fingerprint Identification System Using Crossing Number Concept


Chaudhari, Atul S., Patnaik, Girish K., Patil, Sandip S., Informatica Economica


(ProQuest: ... denotes formulae omitted.)

1 Introduction

In the field of biometric identification, fingerprints are the most widely used bio-metric feature for person identification and verification. The fingerprint of every indi-vidual is considered to be unique. No two persons have the same set of fingerprints; al-so finger ridge patterns do not change throughout the life of an individual. This property makes fingerprints an excellent bi-ometric identifier. Therefore it is one of the popular and effective means for identification of an individual and used as forensic evi-dence. In recent years, significant perfor-mance improvements have been achieved in commercial automatic fingerprint recognition systems.

Biometric Systems are systems that use dis-tinctive anatomical (e.g., fingerprints, face, iris) and behavioral (e.g., speech) characteris-tics, called biometrics traits, to automatically recognize individuals. The word biometrics is derived from the Greek words bios (mean-ing life) and metron (meaning measurement); biometric identifiers are measurements from living human body [3]. Perhaps all biometric identifiers are a combination of anatomical and behavioral characteristics and they should not be exclusively classified into ei-ther anatomical or behavioral characteristics. For example, fingerprints are anatomical in nature but the usage of the input device de-pends on the person's behavior. Thus, the in-put to the recognition engine is a combina-tion of anatomical and behavioral character-istics. Fingerprints are the patterns formed on the epidermis of the fingertip. Fingerprints are made up of series of ridges and valleys (also called as furrows) on the surface of the fingertip and have core around which pattern like swirls, loops or arches are curved to en-sure that each print is unique [3]. The inter-leaved pattern of ridges and valleys are the most evident structural characteristic of a fingerprint. The ridges are the single curved segment and valleys are the region between two ridges. The most commonly used finger-print features are minutiae. Minutiae are the discontinuities in local ridge structure. They are used by forensic experts to match two fingerprints. There are about 150 differ-ent types of minutiae [7]. Among these mi-nutiae types "ridge ending" and "ridge bifur-cation" are the most commonly used as all the other types of minutiae are combinations of ridge endings and ridge bifurcations. A ridge ending is defined as the ridge point where a ridge ends abruptly [3]. A ridge bi-furcation is defined as the ridge point where a ridge forks or diverges into branch ridges [3]. Some common types of minutiae are shown in Figure 1.

2 Related Work

Lili Liu and Tianjie Cao proposed an effi-cient verification system based on biomet-rics. In this system they have used Gabor fil-ter based Enhancement and CN concept for Minutiae Extraction [1].

Lin Hong et al. have designed and imple-mented an Identity Authentication system which operates in two stages: minutiae ex-traction and minutiae matching. An align-ment-based elastic matching algorithm is proposed for minutiae matching [9].

Manvjeet Kaur et al. proposed Fingerprint Verification System using Minutiae Extrac-tion Technique. In this System they have in-troduced combined methods to build a minu-tia extractor and a minutia matcher. Segmen-tation with Morphological operations used to improve thinning, false minutiae removal, minutia marking. For this system they have used Histogram Equalization and FFT for fingerprint image enhancement and CN Con-cept for Minutiae Extraction [8].

F. A. Afsar et al. presented the minutiae based Automatic Fingerprint Identification Systems. The technique is based on the ex-traction of minutiae from the thinned, binarized and segmented version of a finger-print image. The system uses fingerprint classification for indexing during fingerprint matching which greatly enhances the perfor-mance of the matching algorithm [5]. …

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