Advanced System for Management and Recognition of Minutiae in Fingerprints

Transcription

1 Advanced System for Management and Recognition of Minutiae in Fingerprints Angélica González, José Gómez, Miguel Ramón, and Luis García * Abstract. This article briefly describes the advanced computer system designed for the recognition of minutiae in fingerprints digital images. The system provides both automatic and manual extraction of relevant data from the fingerprints images, storing that information in a database. Provides statistical calculations, including calculations for cumulative frequency analysis; this is an important parameter for calculating distinction rates. The system is enabled to differentiate by sex, finger, fingerprint type and sector that has been divided the dactylogram. Keywords: Minutiae recognition, Automatic minutiae extraction, Fingerprints statistical calculation, AFIS (Automated Fingerprint Identication System), Dactylogram. 1 Introduction The main objective is developing a comprehensive management system for fingerprints digitalized images and minutiae; extracting the minutiae automatically and providing functionality to system users to make manual corrections at any time. All these operations will be recorded in the system database, which will compile the information of the minutiae and fingerprints for subsequent statistical analysis. The second objective is to seek a complementary method for fingerprint identification by the automatic calculation of frequencies of the characteristic points, the minutiae are located by determining the number of ridges that are crossed by an imaginary line that connects each minutia and fingerprint center and also by a Cartesian grid that can generate a variable size environment defined by the user. Angélica González José Gómez Miguel Ramón Luis García Computers and Automation Department, Universidad de Salamanca, Salamanca, Spain {angelica,marin,luisjaviergs}@usal.es, miguel.ramon@dgp.mir.es A. Abraham et al. (Eds.): International Symposium on DCAI, AISC 91, pp springerlink.com Springer-Verlag Berlin Heidelberg 2011

2 36 A. González et al. 2 System Description The system for management and minutiae recognition has been implemented as an application that lets the user interact with digital images of fingerprints of many kinds, including WSQ format designed by the FBI, which is a compressed format specially dedicated to fingerprints;. The application consists of a main program incorporating a number of libraries where all the features of the system are implemented, there is also a communication with a database that stores information of individuals, their fingerprints stored into digital images and the minutiae extracted of the fingerprints. 3 Processing Digital Images of Fingerprints The application can perform a complete fingerprint image processing; including a large number of filters and operations to carry out treatment and enhancement of the images. It is very important that the quality of the images managed by the system is high, meaning as quality tha the images are not damaged and, therefore, that the ridges and valleys of the fingerprints are well differentiated. Ideally, the dactylogram presents this quality from the source for high rates of success in extracting minutiae; otherwise the filtering functions can overcome some shortcomings of the image source. Among the filtering techniques are include: [1] Smooth: This filter repair damaged or low quality fingerprints images, softening gray boundaries of the entire image. The smoothing filter, as others used in the system, can be viewed as a 2D linear filter. The filters in two dimensions, using a convolution matrix whose form is generally 3x3 or 4x4 matrixes where the filter coefficients are stored. Medium: A medium-type filter is a nonlinear filter that calculates the median value of replacing each image by the median of the values that surround it in a window. The window size is usually 5x5. This filter has been used in the system to clean the smudges from the edges of the ridges and valleys contained in the image of dactylogram. 4 Extraction of Minutiae in Fingerprints Images The application enables automated and manual minutiae extraction in a dactylogram. Before the system user can extract the minutiae, the user must manually enter the center of the dactylogram generating a Cartesian axes grid, typically of 0.75 mm, because to 1 mm can be more than 1 minutia point [2,3] this value is configurable by the system user. For automatic extraction some operations are performed in the fingerprint image, which consist of preprocessing, which cleanses the image of its imperfections, getting the last item on the thinning image, at this point the image is prepared to search automatically the characteristics that make it unique from any other.

3 Advanced System for Management and Recognition of Minutiae in Fingerprints 37 Fig. 1 Extracting minutiae operations of a dactylogram. This technique is known as classical extraction [4], it performs a preprocessing of the image before detecting the minutiae. Once done the system look for patterns to identify the fingerprint image, in which the width of the ridges is one pixel. Dactylogram preprocessing operations make the extraction algorithm can work with a wide range of qualities; the price paid is the time to perform this preprocessing. To extract the characteristic points first a sweep of the entire image preprocessed is made to detect those points that are candidate sites by analyzing patterns around them that make up a resizable window, and after each point determines its orientation to identify the subtype. Fig. 2 Minutiae extracted by the system on a section of a dactylogram The system is able of distinguishing between eighteen different characteristic points, as shown in figure 3. Initial ridge ending Terminal ridge ending Superior ridge ending Inferior ridge ending Bifurcation Opposed bifurcation Superior bifurcation Inferior bifurcation Deviation Bridge Wedge Island (short ridge) Interruption Lake (enclosure) Short Ridge (Dot) Ridge crossing Hook (spur) Other morphologies Fig. 3 Morphology of minutiae

4 38 A. González et al. Information concerning the minutiae is stored in the system database, the most important aspects that are saved are the type of minutiae, point location within the dactylogram, this location is stored in different scales; in pixels of the image on the grid X and Y axis of the Cartesian grid whose center is identified by the user on the image of dactylogram also calculated and automatically stores information on the number of ridges crossing a imaginary line connecting the minutia with the center of dactylogram. Fig. 4 Minutiae with Cartesian axes and grid lines at the center of a dactylogram There are not a large number of computer systems that manage and work on alternative methods that are raised in this paper. However, studies [5] that have a similar approach to the application of filters to reduce noise dactylogram image, but about the minutiae extraction using wavelet transformation in analysis of sub windows of the image around the Core Point of the dactylogram. In the system described in this paper we opted for a classical approach of pattern matching, because it was identified the need to work with partial fingerprints where the Core Point could not be present and the range of types of minutiae to be extracted was large. Comparative minutiae detection algorithms based on the quality of the source image [6] indicate that the ridge valley clarity approach, as used in the system preprocessing of the image fingerprint implemented with local thinned clarity score, have the advantage of that clarity between ridges and the valleys can be calculated by counting the misclassified pixels, while the weakness is in the region of high curvature as in some points of singularity. This extraction technique is among the best results for a wide range of fingerprint image qualities. 5 Generation of Statistics With the minutiae extraction data stored the system is capable of generating a wide range of statistics that allows further study of the fingerprints processed by the system to obtain relevant information to indicate whether the fingerprints oscillate significantly between men and women, each of the fingers or by classification of the same fingerprint. The computer application is able to elaborate the following statistics: 1. Graphs and tables of the frequencies of different minutiae as the number of ridges that separate them from the central point.

5 Advanced System for Management and Recognition of Minutiae in Fingerprints 39 Fig. 5 Graph and frequency data by the number of ridges 2. Graphics and percentage frequency tables of individual minutiae as the number of ridges that separate them from the central point. Fig. 6 Graphic and percentage frequency data the number of ridges 3. Graphs and tables of the frequencies of different minutiae by their location in grid. Fig. 7 Chart and data grid frequency

6 40 A. González et al. 4. Graphs and tables of percentage frequencies of the characteristic points by their location in grid. Fig. 8 Graphic and percentage frequency data of the number of ridges 5. Calculation of cumulative frequency of the selected points as they are marking. [7]. Fig. 9 Cumulative frequencies Regarding the above tables and graphs can discriminate by sex (man and woman), finger (between 10 fingers), type (plain, central pocket, double loop, accidental) and that sector has been divided dactylogram and for each of the estimated minutiae.

7 Advanced System for Management and Recognition of Minutiae in Fingerprints 41 Fig. 10 Dialog for statistical discrimination 6 Conclusions and Lines of Future Work The most important and complex process of implementation is the extraction of the minutiae of fingerprints; This automatic process is based on a minutiae detection algorithm on the preprocessed image using features of automatic pattern recognition in digital images. As success rates of these automated processes are moderate, especially if dactylogram quality is not high, can be further improved in future work. Besides the characteristic points shown can be manually edited by the user and the centre of dactylogram to make corrections if consider necessary. The generation of stored data statistics can be considered as the ultimate goal of the system. Thus, the application offer the possibility to make statistical studies o fingerprints stored in the system to obtain important information on whether the characteristic points oscillate significantly between men and women, each of the fingers or according to their classification. You can also calculate the frequency of each of the minutiae and also the cumulative frequencies of a set of selected points, the calculation to ascertain the rates of discrimination in fingerprint identifications. When dactylogram also has a classified, allowing comparison with other fingerprints stored in the database, showing some statistics indicating the degree of similarity between compared fingerprints. It is recommended further improve of the automatically extraction of minutiae to be more precise, accurate and efficient, here is expected that the use of neural networks is the best option [8] especially in low-quality images, but is also expected that the high success rates cannot be generalized to all fingerprints, since the results are not guaranteed. The evolution of the application also goes for using

8 42 A. González et al. it in a client-server environment with different users access to a common database, strongly considering the security requirements for remote access [9]. References [1] Arsuate, G.A., Nasisi, O.H., Martin, M.: Recognition of features in fingerprints for human identification. Universidad Nacional de San Juan. Faculty of Engineering. Automation Institute (1997) [2] Sclove "The, S.L.: occurrence of fingerprints characteristics as two dimensional processes. Journal of the American Statistical Association 74(367) (1979) [3] Trauring, M.: Automatic comparasion of finger-ridge patterns. Natura, (1963) [4] The fingerprint identification (Prodac + C Fits), [5] Janjua, F., Javed, M.Y., Sarfraz, N.: ch.19: Hybrid Fingerprint Verification System Based on Fusion of Feature Extraction and Minutiae Detection Strategy. In: 3rd International Conference on Geometric Modeling and Imaging, GMAI 2008, July 9-11, pp (2008) [6] Jin, C., Kim, H., Cui, X., Park, E., Kim, J., Hwang, J., Elliott, S.: Comparative Assessment of Fingerprint Sample Quality Measures Based on Minutiae-Based Matching Performance. In: Second International Symposium on Electronic Commerce and Security, May 22-24, vol. 1, pp (2009), doi: /isecs [7] Study on the frequency of appearance of the minutiae in fingerprints, FRAPUC. Commissioner General of Police Science. General Directorate of Police and G.C. Date of publication (2002) [8] Bajo, J., De Paz, J.F., Rodríguez, S., González, A.: Hierarchical neural network for clustering and classification. Logic Journal of the IGLP, ISSN , ISSN [9] Pinzón, C.I., De Paz, J.F., Rodríguez, S., Corchado, J.M., Bajo, J.: A hybrid agentbased classification mechanism to detect denial of service attacks. Journal of Physical Agents (JoPHA) 3(3), (2009), doi: ISSN: