Fingerprint Orientation Estimation Research Articles

An improved fingerprint orientation field extraction method based on quality grading scheme

Orientation pattern is an important feature for characterizing fingerprint and plays a very important role in the automatic fingerprint identification system (AFIS). Conventional gradient based methods are popular but very sensitive to noise. In this paper, we present an improved fingerprint orientation field (FOF) extraction method based on quality grading scheme. In order to effectively remove the noise, the point orientations are fitted by using 2D discrete orthogonal polynomial. The role of the gradient modulus is taken into full account, and the weights of the point orientations are obtained by computing the similarity of the fitted point orientations. The block qualities are assessed by the coherence of point orientations and the block orientations are estimated based on quality grading scheme. In the proposed method, it does not need any prior knowledge of singular points. To validate the performance, the proposed method has been applied to fingerprint singularity detection and fingerprint recognition. We compared the proposed method with other state-of-the-art fingerprint orientation estimation algorithms. Our statistical experiments show that the proposed method can significantly improve in both singular point detection and matching rates, and it is more robust against noise.

RELIABLE ORIENTATION FIELD ESTIMATION OF FINGERPRINT BASED ON ADAPTIVE NEIGHBORHOOD ANALYSIS

Fingerprint Orientation estimation is an important step in feature extraction and classification. However, a reliable extraction of fingerprint orientation data is still a challenge for poor quality images. In this paper, a gradient based estimation of orientation field based on the analysis of orientation consistency in the neighborhood for regularizing the orientation field is proposed. Experimental results are analyzed and compared with other existing gradient based methods used in this work. Evaluation performed on standard FVC2002 fingerprint databases DB1, DB2 and sample fingerprint images collected using optical fingerprint reader exhibit visibly better orientation estimation for various quality images using the proposed method.

Implementation of modified polar complex moments-based fingerprint orientation estimation for effective segmentation

Segmentation is an important step in deciding the performance of fingerprint identification systems. In this paper, we present the modified polar complex moments MPCMs fingerprint orientation estimation algorithm, capable of describing the fingerprint flow structures including singular point regions in the fingerprint images effectively. To discard the background region of the low-quality fingerprint images, regularisation was employed. These algorithms are tested on various types of fingerprint images containing low-quality unrecoverable region and the results obtained from the proposed method were compared with those obtained from well-known gradient-based and PCMs methods. The proposed method was also used to study the contrast enhancement process with our previously developed modified histogram equalisation MHE based on adaptive inverse hyperbolic tangent AIHT method. The MPCMs method exhibits better segmentation than the traditional methods in both normal and low-contrast image segmentations, as evident from the estimated matching scores as well as ROC graph.

Fingerprint ridge orientation field reconstruction using the best quadratic approximation by orthogonal polynomials in two discrete variables

This paper proposes a novel algorithm for reconstructing the fingerprint orientation field (FOF). The basic idea of the algorithm is to reconstruct the ridge orientation by using the best quadratic approximation by orthogonal polynomials in two discrete variables. We first estimate the local region orientation by the linear projection analysis (LPA) based on the vector set of point gradients, and then reconstruct the ridge orientation field using the best quadratic approximation by orthogonal polynomials in two discrete variables in the sine domain. In this way, we solve the problem that is difficult to accurately extract low quality fingerprint image orientation fields. The experiments with the database of FVC 2004 show that, compared to the state-of-the-art fingerprint orientation estimation algorithms, the proposed method is more accurate and more robust against noise, and is able to better estimate the FOF of low quality fingerprint images with large areas of noise.

A review on fingerprint orientation estimation

AbstractFingerprint orientation plays important roles in fingerprint enhancement, fingerprint classification, and fingerprint recognition. This paper critically reviews the primary advances on fingerprint orientation estimation. Advantages and limitations of existing methods have been addressed. Issues on future development have been discussed. Copyright © 2010 John Wiley & Sons, Ltd.

Modelling fingerprint ridge orientation using Legendre polynomials

The estimation of fingerprint ridge orientation is an essential step in every automatic fingerprint verification system. The importance of ridge orientation can be deflected from the fact that it is inevitably used for detecting, describing and matching fingerprint features such as minutiae and singular points. In this paper we propose a novel method for fingerprint ridge orientation modelling using Legendre polynomials. One of the main problems it addresses is smoothing orientation data while preserving details in high curvature areas, especially singular points. We show that singular points, which result in a discontinuous orientation field, can be modelled by the zero-poles of Legendre polynomials. The models parameters are obtained in a two staged optimization procedure. Another advantage of the proposed method is a very compact representation of the orientation field, using only 56 coefficients. We have carried out extensive experiments using a state-of-the-art fingerprint matcher and a singular point detector. Moreover, we compared the proposed method with other state-of-the-art fingerprint orientation estimation algorithms. We can report significant improvements in both singular point detection and matching rates.