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Minutia matching is the most popular approach to fingerprint recognition. In this paper, we analyzed a novel fingerprint feature named adjacent orientation vector, or AOV, for fingerprint matching. In the first stage, AOV is used to find possible minutiae pairs. Then one minutiae set is rotated and translated. This is followed by a preliminary matching to ensure reliability as well as a fine matching to overcome possible distortion. Such method has been deployed to a payroll and security access information system and its workability is encouraging. The information system aims to offer a highly secured and automated identification system for payroll tracking as well as authorized access to working areas.

Because of uniqueness, as a personal identification method, fingerprint has been widely used in the past decades. The most popular matching strategy for fingerprint identification is minutiae matching. The simplest pattern of the minutiae-based representations consists of a set of minutiae, including ridge endings and bifurcations defined by their spatial coordinates. Each minutia is described by its spatial location associated with the orientation. Although a set of minutiae has been widely used for matching, the noise problem in a fingerprint image has not been solved. The disadvantage of minutiae based method is the lack of robustness, there are some alternative methods proposed, for instance, Jain’s filterbank method and Isenor and Zaky’s graph matching method. The feature vector of minutia generally consists of the minutia type, the coordinates and the tangential angle of the minutia. The automatic fingerprint verification/identification is then achieved with a kind of point pattern matching instead of the fingerprint image matching. Several point pattern matching algorithms have been proposed in the literature. The point pattern matching is generally intractable because the correspondences between the two point sets of template and input fingerprint are unknown. The minutia correspondences are difficult to obtain due to several factors such as the rotation, translation and deformation of the fingerprints, the location and direction errors of the detected minutiae as well as the presence of spurious minutiae and the absence of genuine minutiae.

This package uses Peter Kovesi's code for fingerprint enhancement, "MATLAB and Octave Functions for Computer Vision and Image Processing" and it is based on the paper "Adjacent orientation vector based fingerprint minutiae matching system", G. S. Ng, X. Tong, X. Tang and D. Shi, Pattern Recognition, ICPR 2004. This article is available at

We have tested the code with Set "A" of FVC2004 Database, using 100 classes, N fingerprint images randomly selected for training (totally N*100 images) and 8-N fingerprint images used for testing (totally 800-N*100 images), without any overlapping between training and testing images, obtaining the following results (R is the recognition rate):

  • N = 4, R = 88.40%
  • N = 2, R = 79.48%
  • N = 1, R = 60.59%

Index Terms: Matlab, source, code, adjacent, orientation, vector, fingerprint, minutiae, matching, system, recognition, verification, identification.






Figure 1. Fingerprint sensor

A simple and effective source code for Fingerprint Identification.

Demo code (protected P-files) available for performance evaluation. Matlab Image Processing Toolbox is required.

Major features


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The authors have no relationship or partnership with The Mathworks. All the code provided is written in Matlab language (M-files and/or M-functions), with no dll or other protected parts of code (P-files or executables). The code was developed with Matlab 14 SP1. Matlab Image Processing Toolbox is required. The code provided has to be considered "as is" and it is without any kind of warranty. The authors deny any kind of warranty concerning the code as well as any kind of responsibility for problems and damages which may be caused by the use of the code itself including all parts of the source code.

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