Imperial Journal of Interdisciplinary Research (IJIR) Vol-3, Issue-2, 2017 ISSN: 2454-1362, http://www.onlinejournal.in
An Improved Scheme for Bad-Quality Fingerprint Reconstruction and Matching Arshdeep Singh1 & Gaurav Mittal2 1Pursuing M.Tech, Electronics and communication Engineering 2Assistant Professor, Bhai Gurdas Institute of Engineering and Technology, Sangrur Abstract: Due to growing concerns of authentication and security in the world, biometric security systems are becoming increasingly popular. Fingerprints tend to be the most used biometric feature in most security and identification system. In any fingerprint identification system, enhancement of the fingerprint image prior to recognition is essential. A fingerprint is an impression left by the friction ridges of a human finger. In a wider perspective, fingerprints are the traces of an impression from the friction ridges of any part of a human or other primate hand or foot. Impressions of fingerprints are caused on a surface by the natural secretions of sweat from the eccrine glands that are present in friction ridge skin. These can also made by artificial means such as ink or other substances, transferred from the peaks of friction ridges on the skin to a relatively smooth surface such as a fingerprint card. In this work, a whole fingerprint authentication system has been proposed in which enhancement of fingerprints, minutia point extraction, valid minutia point filtering and matching has been carried out. In this major work presented has been done minutia point extraction and filtering in which wrong or invalid minutia points has been removed which results in better accuracy of the authentication system. Experimental results show that the proposed system work effectively on all types of fingerprint images and evaluates valid minutia points from the input fingerprint image. Keywords: Fingerprint image enhancement, feature extraction, classification, matching 1.1 Fingerprints as a Biometric A fingerprint is an impression of the friction ridges, from the surface of a fingertip. Fingerprints have been used for personal identification for many decades, more recently becoming automated due to advancements in computing capabilities. Fingerprint recognition is nowadays one of the most important and popular biometric technologies mainly because of the inherent ease in acquisition, the numerous sources (ten fingers) available for collection, and the established use and collections by law enforcement agencies. Automatic fingerprint identification is one of the most reliable
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biometric technologies. This is because of the wellknown fingerprint distinctiveness, persistence, ease of acquisition and high matching accuracy rates. Fingerprints are unique to each individual and they do not change over time. Even identical twins (who share their DNA) do not carry identical fingerprints. The uniqueness can be attributed to the fact that the ridge patterns and the details in small areas of friction ridges are never repeated. These friction ridges develop on the fetus in their definitive form before birth and are known to be persistent throughout life except for permanent scarring. Scientific research in areas such as biology, embryology, anatomy and histology has supported these findings [1]. Also, the matching accuracy of fingerprint based authentication systems has been shown to be very high. Fingerprint-based authentication systems continue to dominate the biometrics market by accounting for almost 52% of authentication systems based on biometric traits [2]. 1.2 Fingerprint Image enhancements In this section, we have surveyed the most efficiently techniques used in fingerprint image enhancement as the input images have very low quality, distortions and contrast problems.Galton [3] defined a set of features for fingerprint identification, which since then, has been refined to include additional types of fingerprint features. However, most of these features are not commonly used in fingerprint identification systems. Instead the set of minutiae types are restricted into only two types, ridge endings and bifurcations, as other types of minutiae can be expressed in terms of these two feature types. Ridge endings are the points where the ridge curve terminates, and bifurcations are where a ridge splits from a single path to two paths at a Y-junction. Figure 1 illustrates an example of a ridge ending and a bifurcation. In this example, the black pixels correspond to the ridges, and the white pixels correspond to the valleys.
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Imperial Journal of Interdisciplinary Research (IJIR) Vol-3, Issue-2, 2017 ISSN: 2454-1362, http://www.onlinejournal.in Figure 2: (a) A good quality fingerprint image; (b) a poor quality fingerprint caused by extremely dry skin; (c) a noisy fingerprint image.
Figure 1: Example of a ridge ending and a bifurcation. Fingerprint images are rarely of perfect quality. They may be degraded and corrupted with elements of noise due to many factors including variations in skin and impression conditions. This degradation can result in a significant number of spurious minutiae being created and genuine minutiae being ignored. A critical step in studying the statistics of fingerprint minutiae is to reliably extract minutiae from fingerprint images. Thus, it is necessary to employ image enhancement techniques prior to minutiae extraction to obtain a more reliable estimate of minutiae locations. The quality of the ridge structures in a fingerprint image is an important characteristic, as the ridges carry the information of characteristic features required for minutiae extraction. Ideally, in a welldefined fingerprint image, the ridges and valleys should alternate and flow in locally constant direction. This regularity facilitates the detection of ridges and consequently, allows minutiae to be precisely extracted from the thinned ridges. However, in practice, a fingerprint image may not always be well defined due to elements of noise that corrupt the clarity of the ridge structures. This corruption may occur due to variations in skin and impression conditions such as scars, humidity, dirt, and non-uniform contact with the fingerprint capture device [4]. Thus, image enhancement techniques are often employed to reduce the noise and enhance the definition of ridges against valleys. Figure 2 shows good quality, bad quality and noisy-wet bad quality images.
The most widely used technique for fingerprint image enhancement is based on contextual filters. The parameters of these filters change according to the local context i.e. local ridge frequency and orientation. Such a filter can capture the local information and can use them to efficiently remove the undesired noise (i.e. fill small ridge breaks, fill intra-ridge holes, and separate parallel touching ridges) and preserve the true ridge and valley structure. The filters themselves may be defined in spatial or in the Fourier domain. Sharat et al. [5] uses contextual filtering in Fourier domain. The algorithm consists of two stages. The first stage consists of STFT (Short Time Fourier Transform) analysis and the second stage performs the contextual filtering. The STFT analysis stage yields the ridge orientation image, the ridge frequency image and the region mask. The orientation image represents the instantaneous ridge orientation at every point in the fingerprint image. The orientation image is then used to compute the Coherence Image which contains coherence values of the various regions within the fingerprint. The coherence value is low in regions with points of singularities (core, delta etc.). The coherence image is then used to adapt the angular bandwidth of the directional filter. The resulting contextual information from STFT analysis is then used to filter each overlapping window (B) in the Fourier domain. Finally, the results of each analysis window are tiled to obtain the enhanced image. Hong et al. [6] proposed method using gabor filters which is based on the convolution of the image with Gabor filters tuned to the local ridge orientation and ridge frequency. The main stages of this algorithm include normalization, ridge orientation estimation, ridge frequency estimation and filtering. 1.3 Existed work in feature extraction, featuring matching phase etc. Blayvas et al. [7] handled the problem of binarization of gray-level images acquired under non-uniform illumination. They state that earlier works construct a threshold surface by interpolating the image gray levels at the points where the image gradient is high. The rationale is that high image gradient indicates probable object edges, and the image values are between the object and the background gray levels. The threshold surface was determined by successive overrelaxation as the solution of the place equation.
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Imperial Journal of Interdisciplinary Research (IJIR) Vol-3, Issue-2, 2017 ISSN: 2454-1362, http://www.onlinejournal.in Hence, their work proposes a different method to determine an adaptive threshold surface. In their new method inspired by multiresolution approximation, the threshold surface is constructed with considerable lower computational complexity and is smooth, yielding faster image binarization and better visual performance. Sen et al. [8] presented useful and effective fingerprint image segmentation. They extract two new features with which their algorithm can distinguish noisy area from the foreground and therefore reduce the number of false minutiae. They use supervised RBF neural network to classify patterns and select typical patterns to train the classifier. Their experimental results show a significant improvement in fingerprint segmentation performance.
nonlinear bidimensional approximation direction-map distribution of feature space.
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Sagar et al. [12] presented an approach of fingerprint identification based on fuzzy logic techniques. Since the whole procedure of the fingerprint verification systems is very computationally expensive and hence requires more expensive hardware to meet the responsetime requirements, they developed a matching algorithm that encodes the detected minutiae points in a compressed format initially, and a fuzzy approximation theorem is employed to match these encoded data with the fingerprint under test. This algorithm has the advantage of being simple and less expensive.
Bazen and Otterlo[9] show that reinforcement learning can be used for minutiae detection fingerprint matching. Minutiae are characteristic features of fingerprints which determine their uniqueness. Classical approaches use a series of image processing steps for this task, but lack robustness because they are highly sensitive to noise and image quality. They propose a more robust approach in which an autonomous agent walks around in the fingerprint and learn how to follow ridges in the fingerprint and how to recognize minutiae. The agent is situated in the environment and uses reinforcement learning to obtain an optimal policy.
Pankantiet al. [13] described the design and implementation of a prototype automatic identity authentication system that uses fingerprints to authenticate the identity of an individual. They developed an improved minutiae extraction algorithm claimed to be faster and more accurate than earlier algorithms. An alignment-based minutiae matching algorithm is proposed. This algorithm is capable of finding the correspondences between input minutiae and the stored template without resorting to exhaustive search and has the ability to adaptively compensate for the nonlinear deformations and inexact transformations between an input and template. Both the MSU and the NIST fingerprint databases were used to evaluate the performance of the system.
Cappelli et al. [10] proposed a fingerprint classification algorithm based on the multispace KL transform applied to the orientation field. In that algorithm, the number of classes is denoted by C, the classification accuracy is denoted by Acc, and reject rate is denoted by RR. The classification accuracies reported by the different authors are not different in databases with different numbers of fingerprints, and therefore, they cannot be directly compared. Most of the work in fingerprint classification is based on supervised learning and discrete class assignment using knowledge-based features.
Benget al.[14] investigated the fusion of fuzzy logic and neural network technology in automated fingerprint recognition for the extraction of important fingerprint features. Their results showed that, on average, the fuzzy neural approach is a better alternative. The hybrid model— fuzzy and neural networks model—performs the minutiae extraction in two stages: a fuzzy front-end and neural backend. They concluded that by using such fuzzy neural hybrid model, the fingerprint minutiae extraction is more accurate since fewer false minutiae are identified and more true minutiae identified.
Bernard et al. [11] proposed the use of Kohonen topologic map for fingerprint pattern classification. The learning process takes into account the large intra class diversity and the continuum of fingerprint pattern types. Depending on the fingerprint domain-specific knowledge and the expert approach, they presented an original and intuitive description of the algorithm. They concluded that the selforganizing maps are efficient in fingerprint classification and provided a good
1.4 proposed work
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Steps in algorithm used in present work is described as follows 1. Read fingerprint image and identify ridge and non-ridge area. This has been achieved by creating mask which describes the ridge and nonridge pixels in the image by binarization. 2. Image normalization has been carried out in which ridge regions have zero mean and unit
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Imperial Journal of Interdisciplinary Research (IJIR) Vol-3, Issue-2, 2017 ISSN: 2454-1362, http://www.onlinejournal.in standard deviation. This causes the ridge regions in high intensity region which totally separated from valleys which comes in low intensity 3. Orientations has been calculated for the ridge pixels which gives the direction of the ridges 4. Reliability measure of the orientation has been calculated in order to get the fine ridge regions in the image. So only those ridges taken as reliable that has sharp edges and boundaries so that accurate minutia points can be extracted. 5. Then image has been divided into small blocks and frequency has been calculated for each block. In this step, median frequency has been calculated for each block. If this frequency comes in promised region i.e. enough content in the form of ridges , these blocks will be kept otherwise discarded.
Figure 4: (a) Normalization process in order to separate ridge and non-ridge area (b) Binarized mask generated separating fingerprint marks and background without any ridges
6. Then ridge filter is applied to get ridge pixel enhancements. 7. Image binarization and thinning process is applied to get the ridge and valley pixels in the image. 8. Bifurcations and endings are calculated as minutia points in order to carry out matching. 9. Template has been produced for each image and minutia points has been stored in the database as mat files
Figure 5: mask reliability measures in the fingerprint image
10. Whole process is carried out for all databases images taking different individual dataset 11. Matching has been performed for different unknown query fingerprint images and correct and in correct score has been calculated to get best matched results 1.5 Results Figure 6: Ridge frequency of different blocks in the image. Above figure shows median ridge frequency in each block.If a ridgefrequency does not come in the limits such that there are no-ridge regions, then median frequency of that block is used as zero value. Figure 3: Input fingerprint image
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Imperial Journal of Interdisciplinary Research (IJIR) Vol-3, Issue-2, 2017 ISSN: 2454-1362, http://www.onlinejournal.in
Figure 7: (a) original image (b) Ridge filtered image
Figure 8: (a) Ridge filtered image (b) Ridge filtered binary image with applied threshold 0 Thinning process is needed to find the minutia points in the image. Different methods have been used by different scholars but we use morphological thinning. Below is the result after thinning process.
Figure 9: Thinned image After a fingerprint image has been enhanced, the next step is to extract the minutiae from the enhanced image. Following the extraction of minutiae, a final image postprocessing stage is performed to eliminate false minutiae.
Figure 10: detected minutia points as endings and bifurcations After this, template has been stored as .dat file in a folder which will be used in next section for matching process.If the matching g against h is performed, the symmetric one (i.e., h against g) is not executed to avoid correlation. All the scores for such matches are composed into a series of Correct Score. Also the first sample of each finger in the database is matched against the first sample of the remaining fingers to compute the False Acceptance Rate. If the matching g against h is performed, the symmetric one (i.e., h against g) is not executed to avoid correlation. All the scores from such matches are composed into a series of Incorrect Score. The graphs for the correct score has been shown below
Figure 11: Bar graphs showing correct score for the test cases while matched with trained images It has been found that the methods gives high correct scores and differentiates the tested data sets from that which are not of similar person. 1.6 Conclusion Fingerprint biometric is getting increasingly employed in commercial, civilian, military, and
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Imperial Journal of Interdisciplinary Research (IJIR) Vol-3, Issue-2, 2017 ISSN: 2454-1362, http://www.onlinejournal.in financial institutions. A fingerprint is formed as an impression of the pattern of ridges on a finger. A ridge is defined as a single curved segment, and a valley is a region that lies in between ridges. Ridge have lower reflectance than a valley, and thus, appear darker. However, from the detection point of view, they both can be detected with a single ridge detector with opposing polarity. Ridges and valleys both run in parallel in most of the fingerprint, however, at some locations they either merge or terminate, resulting in important minutia points. The minutia, the local discontinuities in the regular ridge flow pattern, provides the necessary features for identification. Minutia comes in two types: ridge bifurcations and terminations. Ridge bifurcations are the points where a ridge splits into two branches, and terminations are where the ridge ends. Details such as the type, orientation, and location of the minutiae are taken into account when performing minutiae matching for identification. In this work we have proposed an efficient minutia filtering technique in which bas minutia points has been removed so that accuracy rate in matching can be increased. First of all we enhanced the fingerprint image by ridge filtering which is popular and widely used for this work. After that minutia marking has been done but a mask has been used which provides the pixels where there is not an efficient capturing of fingerprint has been done while taking it through the device. After that a set of images has been tested by matching and correct scores have been found. Experimental results show that the proposed technique can efficiently differentiate the dissimilar fingerprint images from one another. References: [1] Ridges and Furrows - history and science of fingerprint identification, technology and legal issues. http://ridgesandfurrows.homestead.com/fingerprint. html.
evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 8 (1998), 777 [7]Blayvas I, Bruckstein A, Kimmel R (2002) Computation of adaptive threshold surfaces for image binarization. Computer Science Department Technion Institute of Technology, Haifa [8]Sen W, Weiwei Z, Yangsheng W (2002) Features extraction and application in fingerprint segmentation. National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Beijing [9]Bazen A, Gerez S, Poel M, Otterlo M (2002) Reinforcement learning agent for minutiae extraction from fingerprints. Dept. of Computer Science, TKI, University of Twente, Enschede [10]Cappelli R, Maio D, Maltoni D (1999)Fingerprint classification based on multispace KL. Proceedings workshop on automatic identification advances technologies (Auto ID99), Summit (NJ), pp 117–120 [11] Bernard S, Boujemaa N, Vitale D, Bricot C (2000) Fingerprint classification using Kohonen topologic map. ChatouCedex, France [12]Sagar VK, Ngo DBL, Foo KCK (1995) Feature selection for fingerprint identification. University of Essex, Colchester [13] Jain A, Hong L, Pankanti S, Bolle R (1997) An Identity authentication system using fingerprints. Proc IEEE 85(9): 1365–1388 [14]Sagar V, Beng K (1990) Fingerprint feature extraction by fuzzy logic and neural networks. Nanyang Technological University, IEEE, Singapore, pp 1138–1142
[2] Anil K. Jain and David Maltoni. Handbook of Fingerprint Recognition. Springer Verlag New York, Inc., Secaucus, NJ, USA, 2003. [3] Francis Galton. London, 1892.
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[4] A. K. Jain, L. Hong, S. Pankanti, and R. Bolle.An identity authentication system using fingerprints. Proc. IEEE, 85(9):1365–1388, 1997. [5] SharatChikkerur, Alexander N. Cartwright, and VenuGovindaraju. Fingerprint enhancement using STFT analysis. Pattern Recogn., 40(1):198–211, 2007. [6] Hong, L., Wan, Y., and Jain, A. K. Fingerprint image enhancement: Algorithm and performance
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