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FINGER PRINT REGONIZATION USING RIDGE FEATURES Suresh.A,1 Assistant Professor(Sr.Gr)/IT

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FINGER PRINT REGONIZATION USING RIDGE FEATURES Suresh.A,1 Assistant Professor(Sr.Gr)/IT ISSN 2394-3777 (Print) ISSN 2394-3785 (Online) Available online at www.ijartet.com International Journal of Advanced Research Trends in Engineering and Technology (IJARTET) Vol. 3, Special Issue 2, March 2016 FINGER PRINT REGONIZATION USING RIDGE FEATURES Suresh.A,1 Assistant Professor(Sr.Gr)/IT. Department of Information Technology, Sri Ramakrishna Institute of Technology, Coimbatore-641 010. Dr.P.Malathi2 ,Principal, Bharathiyar Institute of Engineering for Women, Salem Abstract— this paper introduces a novel fingerprint matching with sweat pores for its entire length and is anchored to the algorithm using both ridge features and the conventional dermis (inner skin) by a double row of peg like protuberances, minutiae feature to increase the recognition performance against or papillae. Injuries such as superficial burns, abrasions, or cuts nonlinear deformation in fingerprints. The proposed ridge do not affect the ridge features are composed of four elements: ridge count, ridge Structure or alter the dermal papillae, and the original length, ridge curvature direction, and ridge type. These ridge pattern is duplicated in any new skin that grows. An injury that features have some advantages in that they can represent the destroys the dermal papillae, however, will permanently topology information in entire ridge patterns existing between obliterate the ridges. Fingerprint recognition has been widely two minutiae and are not changed by nonlinear deformation of adopted for user identification due to its reliable performance, the finger. For extracting ridge features, it also defines the usability, and low cost compared with other biometrics such as ridge-based coordinate system in a skeletonized image. With the signature, iris, face, and gait recognition. It is used in a wide proposed ridge features and conventional minutiae features range of forensic and commercial applications, e.g., criminal (minutiae type, orientation, and position), this system propose a investigation, e-commerce, and electronic personal ID cards. novel matching scheme using a breadth-first search to detect the Although significant improvement in fingerprint recognition matched minutiae pairs incrementally. Following that, the has been achieved, many challenging tasks still remain. Among maximum score is computed and used as the final matching them, nonlinear distortions, presented in touch-based score of two fingerprints. Thus, it conclude that the proposed fingerprint sensing, make fingerprint matching more difficult. ridge feature gives additional information for fingerprint As shown in Fig1.1, even though these two fingerprint matching with little increment in template size and can be used images are from the same individual, the relative positions of in conjunction with existing minutiae features to increase the the minutiae are very different due to skin distortions. This accuracy and robustness of fingerprint recognition systems. distortion is an inevitable problem since it is usually associated Keywords— Bioinformatics, Ridge, Curvature, with several parameters including skin elasticity, nonuniform Authentication pressure applied by the subject, different finger placement with the sensor, etc. I. INTRODUCTION A fingerprint is the impression made by the papillary II.EXISTING SYSTEM ridges on the ends of the fingers and thumbs. Fingerprints afford an infallible means of personal identification, because the ridge 2.1.1 FINGERPRINT PREPROCESSING arrangement on every finger of every human being is unique and Before extracting the proposed ridge features, need to does not alter with growth or age. Fingerprints serve to reveal an perform some preprocessing steps (see Fig.2.1). These steps individual's true identity despite personal denial, assumed include typical feature extraction procedures as well as names, or changes in personal appearance additional procedures for quality estimation and circular variance estimation. first divide the image into 8 × 8 pixel blocks. Then, the mean and variance values of each block are Fig 1.1 Example of skin distortions. Resulting from age, disease, plastic surgery, or accident. The practice of utilizing fingerprints as a means of identification is an indispensable aid to modern law enforcement. Each ridge of the epidermis (outer skin) is dotted 909 ISSN 2394-3777 (Print) ISSN 2394-3785 (Online) Available online at www.ijartet.com International Journal of Advanced Research Trends in Engineering and Technology (IJARTET) Vol. 3, Special Issue 2, March 2016 Fig.2.1 Overall Preprocessing After performing the preprocessing steps, obtain the steps skeletonized ridges and minutiae information from the calculated to segment the fingerprint regions in the image. fingerprint image. Then define ridge coordinates and extract The Gabor filter is applied to enhance the image and obtain a ridge features between two minutiae. As shown in Fig.2.3, each skeletonized ridge image. Then, the minutiae (end points and ridge-based coordinate system is defined by a minutia (called bifurcations) are detected in the skeletonized image. origin) and vertical and horizontal axes starting from the origin minutia. First, the vertical axis is defined by drawing a line The quality estimation procedure is performed in order to passing through the origin and orthogonal to the orientation of avoid extracting false minutiae from poor quality regions and to the origin. The axis also traverses the ridge flows orthogonally. enhance the confidence level of the extracted minutiae set. Furthermore, in regions where ridge flows change rapidly, such Vs = sign (O × Vn) as the area around a singular point, it is hard to estimate the ridge orientations accurately or to extract the thinned ridge where Vs, O and Vn represent the sign of the vertical patterns consistently. Therefore, to detect regions which have large curvature, then apply circular variance estimation. The circular variance of the ridge flows in a given block is Calculated as follows :Var (θ ) =1-1/n [ ( )2 + ( ) 2] Where θi and n represent the estimated orientation of th th the i block and the number of neighboring blocks around the i block, respectively. In this project, it uses eight neighboring blocks. Quality estimation and circular variance values are used to avoid generating feature vectors in poor quality regions or in regions around singular points. Moreover, some post processing steps to remove falsely extracted ridges, such as short ridges and bridges. So the system can then extract the ridge structures consistently against various noise sources pixel value will not be affected much. The affected value axis, the minutia orientation vector, and the unit vector of the are very less compare than conventional watermarking. vertical axis, respectively. Thus determine the positive and the negative side of the vertical axis by checking the sign value of 2.2 PROPOSED SYSTEM Vs. To represent the relative position of the minutiae according to the origin, horizontal axes should be defined. The PROPOSED RIDGE-BASED COORDINATE SYSTEM horizontal axes are defined as ridges intersecting the vertical axis. To define the sign of each horizontal axis, the cross 2.2.1 RIDGE FEATURE EXTRACTION product between the vectors pointing from the intersection to the vertical and horizontal axes is calculated as follows: Hs = sign (Hn × Vn) where Hs, Hn and Vn represent the sign of the horizontal axis, the vector pointing from the intersection to the horizontal and the vertical axis, respectively. In the ridge-based coordinate system, the ridge features that describe the relationship between the origin (minutia in Fig.2.3) and an arbitrary minutia are described as follows: V = (rc,rl,rcd,rt) Where rc, rl, rcd, and rt represent the ridge count, ridge Fig2.2 Preprocessing steps and Ridge Feature Extraction length, ridge curvature direction, and ridge type, respectively. These four components form a ridge-based feature vector between two minutiae and this feature vector is used in the ISSN 2394-3777 (Print) ISSN 2394-3785 (Online) Available online at www.ijartet.com International Journal of Advanced Research Trends in Engineering and Technology (IJARTET) Vol. 3, Special Issue 2, March 2016 matching process. In the following sections, it than 16 pixels. Therefore, it can set the threshold of the ridge will explain in detail these ridge features were length feature to determine the same fingerprint as 16 pixels. selected and the methods for extracting these features. The ridge length value also has a sign and follows the RIDGE FEATURE EXTRACTION sign of the related horizontal axis to improve the discriminating power. In the general ridge count methods the number of ridges that intersect the straight line between two minutiae in the To use more topology information in ridge patterns for matching, the ridge curvature direction is also considered. As shown in Fig.2.5, even though the ridge count and ridge length values are very similar, the shapes of the ridge patterns may be different [Fig.2.5 (a), Fig.2.5 (b)]. The ridge curvature direction is defined as follows: rcd=sign( i × Vi -1 ) th Where vi represents the i vector between the sampling points along the horizontal axis from the intersection of the vertical and horizontal axes to the minutia (see Fig.2.5) and represents the number of sampling points. In this project, set the Fig.2.4 Examples of ridge-counting errors using the general sampling point every 8 pixels on the ridges. Then, by checking ridge counting methods the sign of this value, we can determine the ridge
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