English

Abstract

Biometric system is essentially a pattern recognition system which recognizes a person by determining the authenticity of a specific physiological (e.g., fingerprints, face, retina, iris) or behavioral (e.g., gait, signature) characteristic possessed by that person. Among all the presently employed biometric techniques, fingerprint identification systems have received the most attention due to the long history of fingerprints and its extensive use in forensics. Fingerprint is reliable biometric characteristic as it is unique and persistence. Fingerprint is the pattern of ridges and valleys on the surface of fingertip. However, recognizing fingerprints in poor quality images is still a very complex job, so the fingerprint image must be preprocessed before matching. It is very difficult to extract fingerprint features directly from gray scale fingerprint image. In this paper we have proposed the system which uses minutiae based matching algorithm for fingerprint identification. There are three main phases in proposed algorithm. First phase enhance the input fingerprint image by preprocessing it. The enhanced fingerprint image is converted into thinned binary image and then minutiae are extracted by using Crossing Number Concept in second phase. Third stage compares input fingerprint image (after preprocessing and minutiae extraction) with fingerprint images enrolled in database and makes decision whether the input fingerprint is matched with the fingerprint stored in database or not.Keywords: Fingerprint Identification, Minutiae Extraction, Crossing Number Concept(ProQuest: ... denotes formulae omitted.)1 IntroductionIn 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 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 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 endings and bifurcations. …