Implementation of Minutiae Based Fingerprint Identification System Using Crossing Number Concept

Abstract

A biometric system is essentially a pattern recognition system which recognizes a person by determining the authenticity of a specific physiological or behavioral characteristics 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 their extensive use in forensics. However, recognizing fingerprints in poor quality images is still a very complex job. The authors propose a system which uses a minutiae based matching algorithm for fingerprint identification. The first phase enhances the input fingerprint image by preprocessing it. The enhanced fingerprint image is converted into a thinned binary image and then minutiae are extracted by using the Crossing Number Concept in the second phase. Third stage compares the processed fingerprint image with fingerprint images enrolled in a database and makes a decision on whether the input fingerprint matches any of the fingerprints stored in database or not.

Headnote

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 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].

Ishpreet Singh Virk and Raman Maini have used histogram equalization for fingerprint image enhancement, segmentation using Morphological operations, minutia marking by specially considering the triple branch counting, branch into three terminations, an alignment-based elastic matching algorithm minutia unification by decomposing has been developed for minutia matching were implemented. The proposed alignment- based elastic matching algorithm is capable of find-ing the correspondences between minutiae without resorting to exhaustive search [10].

Weiping Chen and Yongsheng Gao presented a new minutiae-based fingerprint matching algorithm using phase correlation. They de-fine a new representation called Minutiae Di-rection Map (MDM). First, they convert mi-nutiae sets into 2D image spaces. Then the transformation parameters are calculated us-ing phase correlation between two MDMs to align two fingerprints to be matched. The similarity of two fingerprints is determined by the distance between two minutiae sets [11].

Anil Jain et al. develop a matcher that utilizes Level 3 features, including pores and ridge contours, for 1000dpi fingerprint matching. Level 3 features are automatically extracted using wavelet transform and Gabor filters and are locally matched using the ICP algo-rithm. Their experiments on a median-sized database show that Level 3 features carry significant discriminatory information. EER values are reduced (relatively ~20%) when Level 3 features are employed in combina-tion with Level 1 and 2 features [12].

Thrust literature survey and review concludes that existing methods are not only facing problems of poor quality fingerprint image but also their dataset dependency.

3 Proposed System

In real world environment, for fingerprint identification it is not always possible to pro-vide idle quality fingerprint image and also it not possible to provide homogeneous dataset. These major challenges in fingerprint identification is accepted and overcome in pro-posed system.

Proposed fingerprint identification system is mainly divided into three modules like fin-gerprint image preprocessing, minutiae ex-traction and minutiae matching as shown in Figure 2.

The input fingerprint image is processed for skeleton image by the fingerprint image pre-processing stage and subsequently processed by minutiae extraction stage for extracting minutiae using crossing number concept. Af-ter minutiae extraction stage if input finger-print image is processed for enrollment then the skeleton image is saved as template fin-gerprint image in database, otherwise skele-ton image is given to matching stage. In matching stage system compares skeleton image with template fingerprint images from database and make decision whether input fingerprint matched or not.

The working of proposed system is as fol-lows:

Step 1: Load the input fingerprint image.

Step 2: Perform normalization on the input fingerprint image to standardize the intensity values in fingerprint image.

Step 3: Perform segmentation using Vari-ance Thresholding method on Normalize fin-gerprint image to separate foreground region and background region.

Step 4: Next, enhance the fingerprint image using Gabor filter.

Step 5: After enhancement convert the image into binary image using global threshold of zero method.

Step 6: Perform thinning operation on binary image to create skeletonised version of the binary image.

Step 7: Extract minutiae from thinned image using crossing number concept and go to step 8 if input fingerprint image is processed for enrollment otherwise go to step 9 if it is pro-cessed for identification.

Step 8: If input fingerprint image is pro-cessed for enrollment then enroll it into data-base.

Step 9: Use minutiae based algorithm to match input fingerprint image with all tem-plate images stored in database if matching is successful then fingerprint is identified, dis-play information related to matched finger-print otherwise it is not identified.

The main modules of proposed system are explained in following sections.

3.1 Normalization

In this stage normalization is performed on input fingerprint image. Normalization is used to standardize the intensity values in fingerprint image by adjusting the range of grey-level values so that it lies within a de-sired range of values. Let I (i, j) represent the grey-level value at pixel (i, j), and N (i, j) represent the normalized grey-level value at pixel (i, j). The normalized image is defined as [5]:

... (1)

where

M and V are the estimated mean and vari-ance of I(i; j), respectively, and M0 and V0 are the desired mean and variance values, re-spectively.

3.2 Segmentation

Segmentation is the process of separating the foreground regions in the image from the background regions. In fingerprint image the foreground regions correspond to the finger-print area containing the ridges and valleys, which is our area of interest and background corresponds to the regions outside the bor-ders of the fingerprint area, which do not contain any valid fingerprint information. When minutiae extraction algorithms are ap-plied to fingerprint image due to the back-ground regions of an image, extraction algo-rithm extract noisy and false minutiae. There-fore in this stage segmentation is employed to discard these background regions, which facilitates the reliable extraction of minutiae. In a fingerprint image, the background re-gions generally exhibit a very low grey-scale variance value, whereas the foreground re-gions have a very high variance. Hence a method based on variance Thresholding [7] is used to perform the segmentation. Firstly, the image is divided into blocks and the grey-scale variance is calculated for each block in the image. If the variance of block is less than the global threshold, then the block is assigned to be a background region; other-wise, it is assigned to be part of the fore-ground. The grey-level variance for a block of size W×W is calculated as:

... (2)

where V(k) is the variance for block k, I(i, j) is the grey-level value at pixel (i, j), and M(k) is the mean grey-level value for the block k.

3.3 Fingerprint Image Enhancement

In this stage fingerprint image is enhanced using method employed by Hong et al. [7]. The configuration of parallel ridges and val-leys with well-defined frequency and orienta-tion in a fingerprint image provide useful information which helps in removing undesired noise. Gabor filters have both frequency-selective and orientation-selective properties and have optimal joint resolution in both spa-tial and frequency domains. Therefore, it is appropriate to use Gabor filters as band pass filters to remove the noise and preserve true ridge/valley structures.

3.4 Binarization

Most minutiae extraction algorithms operate on binary images where there are only two levels of interest: the black pixels that repre-sent ridges, and the white pixels that repre-sent valleys. Binarization is the process that converts a grey level image into a binary im-age. This improves the contrast between the ridges and valleys in a fingerprint image, and consequently facilitates the extraction of mi-nutiae.

In this stage grayscale fingerprint image is converted into binary image using a global threshold. [4] The Binarization process in-volves examining the grey-level value of each pixel in the enhanced image and if the value is greater than the global threshold, then the pixel value is set to a binary value one; otherwise, it is set to zero. The outcome is a binary image containing two levels of in-formation, the foreground ridges and the background valleys.

Let I (x, y) represent the intensity value of enhanced grayscale image at pixel position (x, y). Let Tp be the threshold value. In case of fingerprint images Tp represents the dif-ferentiating intensity between the back-ground pixels and ridge pixels. BW(x, y) rep-resent the binary image obtained by the equa-tion.

... (3)

3.5 Thinning

In this stage thinning operation is performed on binary image to create skeletonised ver-sion of the binary image. Thinning is a mor-phological operation that successively erodes away the foreground pixels in binary image until they are one pixel wide [2]. A standard thinning algorithm is employed, which per-forms the thinning operation using two sub-iterations. This algorithm is accessible in MATLAB via the 'thin' operation under the bwmorph function. [2] The skeleton image is then used in the subsequent extraction of mi-nutiae.

3.6 Minutiae Detection

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 3×3 window. The CN value is then computed, which is defined as half the sum of the dif-ferences between pairs of adjacent pixels in the eight-neighborhood. According to Rutovitz the CN for a ridge pixel P is given by: [6] [10]

... (4)

where Pi is the pixel value in the neighbor-hood of P. For a pixel P, its eight neighbor-ing pixels are scanned in an anti-clockwise direction as follows:

After the CN for a ridge pixel has been computed, the pixel can then be classified accord-ing to the property of its CN value. Using the properties of the CN as shown in Table 2, 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.

3.7 Minutiae Matching

Let T and I be the representation of the tem-plate and input fingerprint, respectively. Each minutia is considered as a triplet m = {x, y, θ} that indicates the x, y minutia location co-ordinates and the minutia angle θ:

...

where m and n denote the number of minuti-ae in T and I, respectively.

A minutia m'j in I and a minutia mi in T are considered "matching", if the spatial distance (sd) between them is smaller than a given tolerance r0 and the direction difference (dd) between them is smaller than an angular tol-erance θ0 [3].

4 Experimental Results

The proposed system is implemented using MATLAB 7.10 (R2010a) and tested on data-base which is contains total 440 fingerprint images out of which 160 images are from FVC 2000 (DB1 and DB2)[13], 160 images are from FVC 2002 (DB1 and DB2)[14] and 120 fingerprint images (size 260×300) are scanned using SecuGen Hamster plus. This scanner employs a high-performance and maintenance-free optical sensor. To evaluate the performance of proposed system FAR and FRR of proposed system are calculated. Table 3 shows the FAR and FRR of proposed fingerprint identification system. At first we have enrolled 440 fingerprint images in data-base, and then to calculate FRR of the pro-posed system we have tested the system by giving 440 input fingerprint images for iden-tification. The system is also tested by giving 200 imposter fingerprint images as input.

Figure 4 shows experimental results of vari-ous stages in proposed system. Figure 4(a) shows original images, Figure 4(b) shows fingerprint image after normalization, Figure 4(c) shows fingerprint image after segmenta-tion using variance Thresholding, Figure 4(d) shows resulting fingerprint image after en-hancement using Gabor filter Figure 4(e) shows result if binarisation using global threshold, Figure 4(f) shows skeleton image after applying thinning operation on binary image and Finally Figure 4(g) shows extract-ed minutiae using Crossing Number method.

5 Conclusions and Future Work

The performance of a fingerprint feature ex-traction and matching algorithms heavily de-pends upon the quality of the input finger-print image. Gabor filters has both frequency selective and orientation-selective properties. It is observed that Gabor filter method of fin-gerprint image enhancement is giving better results. Minutiae extraction algorithm can de-tect all the minutiae, including both true and false minutiae, using the Rutovitz Crossing Number (CN) on the skeleton images after thinning stage. The performance of proposed system is evaluated by calculating FAR and FRR. The proposed system has 0% FAR and 0.23% FRR which shows that the accuracy of proposed system is high. The proposed sys-tem is simulated on 2.30 GHz Intel core i5 general purpose system using MATLAB 7.10. The proposed minutiae based finger-print identification shows better performance in identifying the fingerprint, till it takes more time. The time required for detection and recognition could considerable be re-duced by implementing the same system on dedicated hardware using low or middle level language such as C and C++. In future, we will try to implement the same system using C language. Also it is observed that thinning process sometime breaks the ridges which re-sults in increasing number of false minutiae so there is scope to improve thinning algo-rithm so that it will reduce number of false minutiae and increase accuracy of the sys-tem.

References

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