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.