Enhancing fingerprint image recognition algorithm using fractional derivative filters

被引：11

作者：

Baloochian H. ^{[1
]}

Ghaffary H.R. ^{[1
]}

Balochian S. ^{[2
]}

机构：

[1] Department of Computer Engineering, Ferdows Branch, Islamic Azad University, Ferdows

[2] Department of electrical Engineering, Gonabad Branch, Islamic Azad Universaity, Gonabad, Khorasan-e Razavi

来源：

Open Computer Science | 2017年 / 7卷 / 01期

关键词：

Finger print; Fractional derivative of Gabor filter; Image enhancement; Image processing;

D O I：

10.1515/comp-2017-0002

中图分类号：

学科分类号：

摘要：

One of the most important steps in recognizing fingerprint is accurate feature extraction of the input image. To enhance the accuracy of fingerprint recognition, an algorithm using fractional derivatives is proposed in this paper. The proposed algorithm uses the definitions of fractional derivatives Riemann-Liouville (R-L) and Grunwald-Letnikov (G-L) in two sections of direction estimation and image enhancement for the first time. Based on it, new mask of fractional derivative Gabor filter is calculated. The proposed fractional derivative-based method enhances the image quality. This method enhances the structure of ridges and grooves of fingerprint, using fractional derivatives. The efficiency of the proposed method is studied in images of FVC2004 (DB1, DB2, DB3 and DB4) database and the results are evaluated using the criteria including entropy, average gradient, and edge intensity. Also, performance of the proposed method is compared with other technical methods such as Gabor filter. Based on the obtained results from the tests, the method is able to enhance the quality of fingerprint images significantly. © 2017 Hossein Baloochian et al.

引用

页码：9 / 16

页数：7

共 22 条

[1]

Khodadoust J., Khodadoust A.M., Fingerprint indexing based on minutiae pairs and convex core point, Pattern Recognition and Image Analysis, 67, pp. 110-126, (2017)

[2]

Megias D., Qureshi A., Collusion-resistant and privacy-preserving P2P multimedia distribution based on recombined fingerprinting, Expert Systems with Applications, 71, pp. 147-172, (2017)

[3]

Billea P., Grrtza I.L., Cordinga P.H., Sachb B., Vildhrja H.W., Vinda S., Fingerprints in compressed strings, Journal of Computer and System Sciences, 86, pp. 171-180, (2017)

[4]

Loverro A., Fractional Calculus: History, Definitions and Applications for The Engineer: Department of Aerospace and, (2004)

[5]

Ortigueira M., Machado T.J., Fractional signal processing and applications, Signal Processing, 83, pp. 2285-2286, (2003)

[6]

Mathieu B., Oustaloup A., Ceyral C., Fractional differentiation for edge detections, Signal Processing, 83, pp. 2421-2432, (2003)

[7]

Yang Z., Lang F., Yu X., Zhang Y., The construction of fractional differential gradient operator, Journal of Computational Information Systems, 7, pp. 4328-4342, (2011)

[8]

Li B., Xie W., Image denoising and enhancement based on adaptive fractional calculus of small probability strategy, Neu-Rocomputing, 175, pp. 704-714, (2016)

[9]

Hu J., Pu Y., Zhou J., Fractional integral denoising algorithm, Journal of The University of Electronic Science and Technology of China, 41, pp. 706-711, (2012)

[10]

Huang G., Pu Y., Chen Q., Zhou J., Research on image denoising based on fractional order integral, Syst. Eng. Electron, 33, pp. 926-932, (2011)

← 1 2 3 →