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.