Bernard Icip01

FINGERPRINT CLASSIFICATION USING KOHONEN TOPOLOGIC MAP Sylvain BERNARD 1,2 , Nozha BOUJEMAA 1 , David VITALE 2 , Claude BRICOT 2 1 INRIA Rocquencourt BP 105, F-78153 Le Chesnay, France 2 THALES Identification, 41 bd de la Republique, BP 53- 78401 Chatou Cedex, France [email protected] ABSTRACT Self Organizing Maps are efficient and usual for dimension reduction and data clustering. In our present work, we propose the use of Kohonen Topologic Map for fingerprint pattern classification. The learning process takes into account the l arge intra-class diversity and the continuum of fingerprint pattern types. After a brief introduction to fingerprint domain-specific knowledge and the expert approach, we present an original and intuitive description of the algorithm. For a classification bas ed on the global shape of the fingerprint, we adopted a suitable feature space. Indeed we obtained 88% of correct classification on a database composed of 1600 NIST fingerprints. 1. INTRODUCTION Most automatic systems for fingerprint comparison are based on minutiae matching. Minutiae points [1] are terminaisons and bifurcations of the ridge lines that constitute a fingerprint pattern. To demonstrate that two fingerprints originate from the same finger or not, human experts detect the ridge ending and bifurcation points of both fingerprints (Fig.1 and Fig.2) and match the two minutiae sets by superposition to count the number of common points. Two fingerprints are considered to be from the same finger if the number of common points is sufficient, depending on the country’s legislation. Fig. 1 – Ridge Ending Fig. 2 – Ridge Bifurcation For Very Large DataBase applications, since the automatic comparison of fingerprints by matching their minutiae sets is time consuming, matching a query fingerprint with the entire database would be computationally intensive. We match the fingerprint with a subset of the database (reduced search-space) using a fast classification process. A well-known expert classification is based on the global shape of the fingerprints [1] statistically observed in the population (Fig. 3). Left Loop – 32 % Rigth Loop – 32 % Whorl – 32% Arch – 4 % Fig. 3 – Experts' Fingerprint Classification O : Core point ∆ : Delta point Class Number of Cores Number of Deltas Relative Positions L : Left Loop 1 Core 1 Delta Delta is at the right of Core R : Right Loop 1 Core 1 Delta Delta at the left of Core W: Whorl 2 Cores 2 Deltas X A : Arch 0 Core 0 Delta X Fig. 4 : Number of Core(s), Delta(s) and Relative Positions per Fingerprint Class Several approaches have been tested for automatic classification. In [2][3][4], auth ors propose classification algorithms based on the number of cores and deltas, and their relative positions (Fig.4). The major problem with such approaches is that sometimes the delta can be out of the image, especially with small sensors. In [5], Jain, Prabhakar & Hong propose a method that uses only one core point and a circular region around the core. The region of interest is divided into sectors; each sector is normalized and filtered using a bank of Gabor filters to produce a set of filtered images; the variance of gray

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Bernard Icip01