Introduction
Fingerprints are the most reliable human characteristics that can be used for person identification. A fingerprint pattern is composed by ridges and valleys. Ridges present various kinds of discontinuities (minutiae) able to capture the invariant and discriminatory information used to recognize fingerprints. In automatic fingerprint recognition system only ridges bifurcation and ridge ending are commonly used. Automatic finger print matching involves determining the degree of similarity between two fingerprint impression by comparing their ridge structure and /or the spatial distribution of the minutiae points. Everyone is known to have unique, immutable fingerprints. Fingerprint identification is based on two basic premises:
- Persistence: the basic characteristics of fingerprints do not change with time and
- Individuality: the fingerprint is unique to an individual.
A fingerprint is made of a series of ridges and furrows on the surface of the finger. The uniqueness of a fingerprint can be determined by the pattern of ridges and furrows as well as the minutiae points. In the fingerprint recognition system the inputs are fingerprint image and the identification number. First the features like Minutiae, ridges etc will be extracted and then based on these features a corresponding template will be generated. For matching purpose these template will be matched using Euclidean distance algorithm. The system is divided into two major modules: 1. Database Preparation Module, and 2. Fingerprint Verification Module.
Methodology
Human finger prints have been discovered on a large number of archaeological artifacts and historical items. Although these finding provide evidences to show that ancient people were aware of the individuality of fingerprints such awareness does not appear to have any scientific basis (Lee and Gaensslen 2001 and Moenssens 1971). In 1684, the English plant morphologist, Nehemiah Grew, published the first scientific paper reporting his system study on the ridge, furrow, and pore structure in fingerprints. Fingerprint verification is an important biometric technique for personal identification. Everyone is known to have unique, immutable fingerprints. Fingerprint identification is based on two basic premises: 1) persistence: the basic characteristics of fingerprints do not change with time and 2) individuality: the fingerprint is unique to an individual. A fingerprint is made of a series of ridges and furrows on the surface of the finger. The uniqueness of a fingerprint can be determined by the pattern of ridges and furrows as well as the minutiae points.
Our Approach
Among many other available biometric traits fingerprints is the most used biometric modality. The basic reason behind the use of fingerprint biometric is that it is the most proven technique to identify the individual. The fingerprint is basically the combination of ridges and valleys on the surface of the finger. The important steps involved in fingerprint recognition using minutiae matching approach are:
1. Image Enhancement
2. Minutiae Extraction
3. Minutiae Matching
1 Image Enhancement
A fingerprint image is corrupted by various kind of noise such as creases, smudges and holes. It is impossible to recover the true ridge/valley structures in the unrecoverable regions; any effort to improve the quality of the fingerprint image in these regions is futile. Therefore, the reasonable enhancement algorithm is used to improve the clarity of ridges/valley structures of fingerprint images in recoverable regions and to mask out the unrecoverable regions.
Figure 1 Different Steps of Image Enhancement
The main steps of the algorithm include (shown in Figure 1):
-
1) Normalization: An input fingerprint image is normalized so that it has a pre-specified mean and variance.
-
2) Local orientation estimation: The orientation image is estimated from the normalized input fingerprint image.
-
3) Local frequency estimation: The frequency image is computed from the normalized input fingerprint image and the estimated orientation image.
-
4) Region mask estimation: The region mask is obtained by classifying each block in the normalized input fingerprint image into a recoverable or unrecoverable block.
-
5) Filtering: A bank of Gabor filters which is tuned to local ridge orientation and ridge frequency is applied to the ridge-and-valley pixels in the normalized input fingerprint image to obtain an enhanced fingerprint image.
2 Minutiae Extraction
The enhanced fingerprint image is binarized and submitted to the thinning algorithm which reduces the ridge thickness to one pixel wide. The skeleton image is used to extract minutiae points which are the points of ridge endings and bifurcations. The most commonly employed method of minutiae extraction is the Crossing Number (CN) concept. This method involves the use of the skeleton image where the ridge flow pattern is eight-connected. The minutiae are extracted by scanning the local neighborhood of each ridge pixel in the image using a 3x3 window. The CN value is then computed, which is defined as half the sum of the differences between pairs of adjacent pixels in the eight-neighborhood. Using the properties of the CN the ridge pixel can then be classified as a ridge ending, bifurcation or non-minutiae point. For example, a ridge pixel with a CN of one corresponds to a ridge ending, and a CN of three corresponds to a bifurcation.
Figure 2 Steps involved in Minutiae Matching
3 Minutiae Matching
The points are matched between database and query images using Elastic Matching approach.