International Journal of Advanced Research in Computer Science and Software Engineering

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1 ISSN: X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: Increasing The Accuracy Of An Existing Fingerprint Recognition System Using Adaptive Technique Sonam Shukla * Pradeep Mishra Information Technology C.S.V.T.U. Bhilai Computer Science & Engg C.S.V.T.U. Bhilai Abstract Biometrics is the science and technology of measuring and analyzing biological data of human body, extracting a feature set from the acquired data, and comparing this set against to the template set in the database. Biometric techniques are gaining importance for personal authentication and identification as compared to the traditional authentication methods. Biometric templates are vulnerable to variety of attacks due to their inherent nature. When a person s biometric is compromised his identity is lost. In contrast to password, biometric is not revocable. The biometric system operates as verification mode or identification mode depending on the requirement of an application. The verification mode validates a person s identity by comparing captured biometric data with ready made template. The identification mode recognizes a person s identity by performing matches against multiple fingerprint biometric templates. Fingerprints are widely used in daily life for more than 100 years due to its feasibility, distinctiveness, permanence, accuracy, reliability, and acceptability. Fingerprint is a pattern of ridges, furrows and minutiae, which are extracted using inked impression on a paper or sensors. A good quality fingerprint contains 25 to 80 minutiae depending on sensor resolution and finger placement on the sensor. The false minutiae are the false ridge breaks due to insufficient amount of ink and crossconnections due to over inking. It is difficult to extract reliably minutia from poor quality fingerprint impressions arising from very dry fingers and fingers mutilated by scars, scratches due to accidents, injuries. Minutia based fingerprint recognition consists of Thinning, Minutiae extraction, Minutiae matching and Computing matching score. Keywords Ridge Enhancement, Thinning, Binarization, Histogram Equalization, Crossing Number, Minutiae Score. I. INTRODUCTION Fingerprints are today the biometric features most widely used for personal identification. Fingerprint recognition is one of the basic tasks of the Integrated Automated Fingerprint Identification Service (IAFIS) of the most famous police agencies. A fingerprint pattern is characterized by a set of ridgelines that often flow in parallel, but intersect and terminate at some points. The uniqueness of a fingerprint is determined by the local ridge characteristics and their relationships. Most automatic systems for fingerprint comparison are based on minutiae matching. Minutiae characteristics are local discontinuities in the fingerprint pattern and represent the two most prominent local ridge characteristics: terminations and bifurcations. A ridge termination is defined as the point where a ridge ends abruptly, while ridge bifurcation is defined as the point where a ridge forks or diverges into branch ridges (Figure 1). A typical fingerprint image contains about minutiae. An automatic fingerprint image matching process, which enables a personal identification, strongly depends on comparison of the minutiae points of interest (MPOI) and their relationships. Reliable automatic extraction of these MPOI is a critical step in fingerprint classification. The performance of minutiae extraction algorithms relies heavily on the quality of the fingerprint images. The ridge structures in poor-quality fingerprint images are not always well defined and, hence, cannot be correctly detected. This might result in the creation of spurious minutiae and the ignoring of genuine minutiae. Therefore, large errors in minutiae localization may be introduced. In order to ensure robust performance of a minutiae extraction algorithm, an enhancement algorithm, which can improve the clarity of the ridge structures, is necessary. Most of the fingerprint image enhancement methods proposed in the literature are applied to binary images, while some others operate directly on gray-scale images. Fig 1: Typical diagram of the fingerprint enrollment and identification processes 2012, IJARCSSE All Rights Reserved Page 52

2 The motivation behind the work is growing need to identify a person for security. The fingerprint is one of the popular biometric methods used to authenticate human being. The proposed fingerprint verification algorithm is an optimized form of FRMSM(Fingerprint Recognition Using Minutia Score Matching) and provides reliable and better performance than the existing technique. Our contribution to this is that we have to implement the Optimized Fingerprint Recognition using Minutia Score Matching method with the help of MATLAB codes. Minutiae are extracted from the thinned image for both template and input image. Finally both the images are subjected to matching process and matching score is to be computed through which more accurate results could be established. recorded for each imposter attempt when the matching score was greater than the established threshold. (viii) False Non Matching Ratio (FNMR): It is the probability that the system denies access to an approved user is given in an equation (2): Enrollee attempts are implemented by matching each input image with corresponding template image, hence it is one-to-one matching. A False Non-match was recorded when the matching score between an enrollee and its template was less than the established threshold. (ix) Matching Score: it is used to calculate the matching score between the input and template data is given in an equation (3): Where, NT and NI represent the total number of minutiae in the template and input matrices respectively. By this definition, the matching score takes on a value between 0 and 1. Matching score of 1 and 0 indicates that data matches perfectly and data is completely mismatched respectively. Fig 2: Details of a sample fingerprint Fig 3: fingerprints showing minutiae Termination : The location where a ridge comes to an end. Bifurcation : The location where a ridge divides into two separate ridges. Binarization : The process of converting the original grayscale image to a black-and white image. Thinning : The process of reducing the width of each ridge to one pixel Termination Angle : The angle between the horizontal and the direction of the ridge. Bifurcation Angle : The angle between the horizontal and the direction of the valley ending between the bifurcations. False Matching Ratio : It is the probability that the system will decide to allow access to an (FMR) imposter is given in an equation (1): The imposter attempts are implemented by matching each input image with all the template images. False match was II. LITERATURE REVIEW Shlomo Greenberg, Mayer Aladjem and Daniel Kogan in 2002 stated that the techniques based on direct gray-scale enhancement perform better than approaches which require binarization and thinning as intermediate steps. The average error percentage, in terms of dropped, exchanged and false minutiae. Atipat Julasayvake and Somsak Choomchuay states that The optimal core point detecting technique outlined in this paper does require the fairly simple field orientation estimation in its first step. This attempt requires less computational load and increase the chance in locating the core point. The detected core point is not necessarily to be the exact one since the second step has taken care of the matter. The second step works with the smaller window where the core point is enclosed. Ravi. J, K. B. Raja, Venugopal. K. R in 2009 stated that In his paper, they presented Fingerprint matching using FRMSM. The pre-processing the original fingerprint involves image binarization, ridge thinning, and noise removal. Fingerprint Recognition using Minutia Score Matching method is used for matching the minutia points. The proposed method FRMSM gives better FMR values compared to the existing method. Kalyani Mali, Samayita Bhattacharya in 2011 stated that the reliability of any automatic fingerprint recognition system strongly relies on the precision obtained in the extraction process. Extraction of appropriate features is one of the most important tasks for a recognition system. Koichi Ito, Ayumi Morita et. al. in 2005 stated that an efficient fingerprint recognition algorithm using the phase-based image matching. The proposed technique is particularly effective for verifying low-quality fingerprint images that could not be identified correctly by conventional techniques. 2012, IJARCSSE All Rights Reserved Page 53

3 iteration m, the enhanced image, I m enh, is obtained by means of the following equation: (4) Fig 4: Existing Technique for fingerprint preprocessing III. IMPLEMENTATION In this chapter, an Automatic Fingerprint Identification Scheme (AFIS) is presented. The scheme comprises a series of processes as shown in the block diagram of Fig. 2. Without loss of generality, hereafter we denote the image of the fingerprint acquired during enrollment as the template (I T ) and the representation of the fingerprint to be matched as the input image (I inp ). According to Fig. 2, the following processes are applied consequently only on the template image I T : i. Ridge Enhancement ii. Binarization iii. Background Removal iv. Thinning v. Minutiae Extraction and Validation vi. Minutiae Automatic Correspondence. In the following analysis, these processes are described in more details: Fig 6: Histogram Enhancement. Original Image (Left). Enhanced image (Right) Fig 7: Fingerprint enhancement by FFT Enhanced image (left), Original image (right) Fig 5: Proposed Technique for fingerprint Matching 3.1 Ridge Enhancement The ridges of the template image I T (Fig. 3a) are enhanced using the method of oriented diffusion (Hastings, 2007), which is a recursive procedure. At each 3.2 Binarization For the binarization of the resulting enhanced template image, The pre-processing of FRMSM uses Binarization to convert gray scale image into binary image by fixing the threshold value. The pixel values above and below the threshold are set to 1 and 0 respectively. An original image and the image after Binarization are shown in the Figure 2. The second derivative of the filtered image Self-Organizing Maps is estimated in a direction normal to the orientation field. The sign of the second derivative is then examined based on the fact that the second derivative of a function is negative in the area of a local maximum and positive in the area of a local minimum. The output of this step is denoted as I BW. 3.3 Background Removal The first step for the estimation of the background of the binary image, BW I, involves the partition of the image domain into blocks of W W pixels. For each block, the mean value and the variance are calculated. If the variance is above a threshold, the pixels of this block are characterized as foreground pixels and the remaining ones 2012, IJARCSSE All Rights Reserved Page 54

4 as background pixel. Then only the foreground pixels of the binarized fingerprint image are maintained and the background pixels are set to the value 255. The output of this step is denoted as I BR. Fig 8:the Fingerprint image after adaptive binarization Binarized image(left), Enhanced gray image(right) 3.4 Thinning The thinning process is applied on the negative version of I BR. Next the ridges must be thinned to a width of onepixel. In this step two consecutive fast parallel thinning algorithms are applied, in order to reduce to a single pixel the width of the ridges in the binary image. These operations are necessary to simplify the subsequent structural analysis of the image for the extraction of the fingerprint minutiae. The thinning must be performed without modifying the original ridge structure of the image. During this process, the algorithms cannot miscalculate beginnings, endings and or bifurcation of the ridges, neither ridges can be broken. In the last stage, the minutiae from the thinned image are extracted, obtaining accordingly the fingerprint biometric pattern. This process involves the determination of: i) whether a pixel, belongs to a ridge or not and, ii) if so, whether it is a bifurcation, a beginning or an ending point, obtaining thus a group of candidate minutiae. Next, all points at the border of the interest region are removed Matching Matching is a key operation in the current fingerprint identification system. One of the most important objectives of fingerprint systems is to achieve a high reliability in comparing the input pattern with respect to the database pattern. Reliably matching fingerprint images is an extremely difficult problem, mainly due to the large variability in different impressions of the same finger (i.e., large intra-class variations). The main factors responsible for the intra-class variations are: displacement, rotation, partial overlap, non-linear distortion, variable pressure, changing skin condition, noise, and feature extraction errors. Therefore, fingerprints from the same finger may sometimes look quite different whereas fingerprints from different fingers may appear quite similar.the method employed in the research was both SP and minutiae based matching. A minutia matching essentially consists of finding the alignment between the template and the input minutiae sets, that results in the maximum number of minutiae pairings. In Minutiae based matching the similarity between the input and stored template are computed. Fig 10: extracting minutiae. Binarized fingerprint (left), Extracted minutiae (right) Fig 9: Thinning. Binarized fingerprint (left), Thinned fingerprint (right) 3.5. Cancellation of improper minutiae This is an important step of minutiae based fingerprint reorganization system. In this step, the improper minutia which are mainly result of spurious noise of input image, are cancelled. Fig 11: Validation of minutiae. extracted minutiae (left), validated minutiae (right) Given two set of minutia of two fingerprint images, the minutia match algorithm determines whether the two minutia sets are from the same finger or not. An alignment-based match algorithm is used in my project. It includes two consecutive stages: one is alignment stage and the second is match stage. 1. Alignment stage. Given two fingerprint images to be matched, choose any one minutia from each 2012, IJARCSSE All Rights Reserved Page 55

5 image, calculate the similarity of the two ridges associated with the two referenced minutia points. If the similarity is larger than a threshold, transform each set of minutia to a new coordination system whose origin is at the referenced point and whose x-axis is coincident with the direction of the referenced point. 2. Match stage: After we get two set of transformed minutia points, we use the elastic match algorithm to count the matched minutia pairs by assuming two minutia having nearly the same position and direction are identical. IV. RESULTS In order to assess the performance of the proposed automatic fingerprint identification scheme, the Real fingerprint database is used. The database contains fingerprint images from nine different persons, for six fingers of each person and for eight impressions of each finger; thus 432 images in total. Each fingerprint image has size pixels. Two different data sets were used. The first data set (SET I) consists of fingerprint images subject to known transformations, while the second one (SET II) comprises of fingerprint image pairs from the database (unknown transformation). Here is the diagram for Correct Score and Incorrect Score distribution: Blue dot line: FRR curve Red dot line: FAR curve The high incorrect acceptance and false rejection are due to some fingerprint images with bad quality and the vulnerable minutia match algorithm. V. CONCLUSIONS In this chapter, an optimizes algorithm for fingerprint identification is presented. The minutiae of the template image are used as neurons of a neural network and the proposed algorithm detects the set of minutiae in the input image in an iterative way. The main advantage of this method, against other minutiae-based fingerprint recognition methods, is that the minutiae of only the template image have to be estimated. Furthermore the method is error-tolerant in the estimation of the minutiae of the template image. This is a matter of high importance since the precise estimation of minutiae is a difficult task, especially for low-quality fingerprint images. The overall performance of the proposed method was 92%. ACKNOWLEDGEMENT I am thankful to Dr. Kamal Mehta(Head of Department), Computer Science & Engineering for giving thoughtful suggestions during my work. I owe the greatest debt and special respectful thanks to Dr. P. B. Deshmukh (Director), Shri Shankaracharya Group Of Institutions, Bhilai for their inspiration and constant encouragement that enabled me to present my work in this form. Fig 12: Distribution of CorrectScores and Incorrect Scores Red line: Incorrect Score Green line: Correct Scores This indicates the algorithm is capable of differentiate fingerprints at a good correct rate by setting an appropriate threshold value. Fig 13: FAR and FRR curve REFERENCES [1] Bazen A. & Gerez, S. (2002).Systematic Methods for the Computation of the Directional Fields and Singular Points of Fingerprints, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, Issue 7, Jul. 2002,pp ,ISSN: [2] A.M.Bronstein, M.M.Bronstein,R.Kimmel,Expression-invariant 3d face recognition. Proc. Audio- and Video-based Biometric Person Authentication (AVBPA),2003, Lecture Notes in Comp., (2688),pp [3] Chan, K. C. ; Moon, Y. S. & Cheng P.S. (2004). Fast Fingerprint Verification Using Subregions of Fingerprint Images, IEEE Transactions on Circuits and Systems For Video Technology., Vol.14, Issue 1, Jan. 2004,pp , ISSN: [4] J.Gu., J.Zhou, C.Yang,Fingerprint Recognition by Combining Global Structure and Local Cues, IEEE Transactions on Image Processing, 2006, vol. 15, no. 7, pp [5] R.Hastings,Ridge Enhancement in Fingerprint Images Using Oriented Diffusion, IEEE Computer Society on Digital Image Computing Techniques and Applications,2007, pp [6] E. P. Kukula et. al., Effect of Human Interaction on Fingerprint Matching Performance, Image Quality, and Minutiae Count, International Conference on Information Technology and Applications, 2008, pp [7] G.S.Rao et. al., A Novel Fingerprints Identification System Based on the Edge Detection, International Journal of Computer Science and Network Security,2008, vol.8, pp [8] J.Ravi, K.B. Raja, K.R.Venugopal.,Fingerprint Recognition Using Minutia Score Matching International Journal of Engineering Science and Technology,2009,Vol.1(2). [9] R. Sonavane, B.S. Sawant,Noisy Fingerprint Image Enhancement Technique for Image Analysis: A Structure Similarity Measure Approach, Journal of Computer Science and Network 2012, IJARCSSE All Rights Reserved Page 56

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