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.
Orientation field 2D discrete orthogonal polynomials Gradient based weighted averaging Quality grading
This is a preview of subscription content, log in to check access.
The work is partially supported by the National Natural Science Foundation of China under Grant No. 61672522, Anhui Provincial Natural Science Foundation under Grant No. 1708085MF145, the Priority Academic Program Development of Jiangsu Higer Education Institutions (PAPD), and the Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology(CICAEET).
Wen X, Shao L, Xue Y et al (2015) A rapid learning algorithm for vehicle classification. Inf Sci 295(1):395–406CrossRefGoogle Scholar
Xia Z, Wang X, Zhang L et al (2016) A privacy-preserving and copy-deterrence content-based image retrieval scheme in cloud computing. IEEE Trans Inf Forensics Security. doi:10.1109/TIFS.2016.2590944Google Scholar
Zhou Z, Wang Y, Jonathan Q et al (2016) Effective and efficient global context verification for image copy detection. IEEE Trans Inf Forensics Security. doi:10.1109/TIFS.2016.2601065Google Scholar
Maltoni D, Maio D, Jain A, et al. (2009) Handbook of fingerprint recognition, 2nd edn. Springer-Verlag, London, UKCrossRefMATHGoogle Scholar
Gottschlich C (2012) Curved-region-based ridge frequency estimation and curved gabor filters for fingerprint image enhancement. IEEE Trans Image Process 21(4):2220–2227MathSciNetCrossRefMATHGoogle Scholar
Sutthiwichaiporn P, Areekul V (2013) Adaptive boosted spectral filtering for progressive fingerprint enhancement. Pattern Recogn 46(9):2465–2486CrossRefGoogle Scholar
Jirachaweng S, Hou Z, Yau W et al (2011) Residual orientation modeling for fingerprint enhancement and singular point detection. Pattern Recognit 44(2):431–442CrossRefMATHGoogle Scholar
Bian W, Luo Y, Xu D et al (2014) Fingerprint ridge orientation field estimation using the best quadratic approximation by orthogonal polynomials in two discrete variables. Pattern Recognit 47(10):3304–3313CrossRefGoogle Scholar