Fingerprint Image Quality Estimation Method Applied to Embedded Devices

Liu Xiaoqiang, Yuan Guoshun, Qiao Shushan


The existing fingerprint image quality estimation algorithm has high computational complexity. The computing resources of embedded devices are limited,and fingerprint image quality estimation algorithm with high computational complexity is difficult to apply on these devices. In order to solve this problem,a method of fingerprint image quality estimation method applied to embedded devices is proposed. First,the method estimates the gradient field,the direction field and the frequency field,and the relationship among the fingerprint image quality,gradient,direction and frequency is obtained by analyzing the high-quality fingerprint image and texture image. This relationship is used to measure the accuracy of the estimated gradient,direction and frequency,and then it is made as the fingerprint image quality index to characterize the quality of fingerprint image. The experimental results show that the method can accurately generate the fingerprint image quality index. The method can distinguish the good and bad regions of the fingerprint image very well. Under the premise of ensuring the performance of the fingerprint authentication system,the computational complexity of the fingerprint quality estimation algorithm is significantly reduced.



Keywords:  fingerprint identification,  image quality,  directional estimation,  frequency estimation

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MAJID K,NARGES A,DIMITRIOS H. Liveness detection and automatic template updating using fusion of ecg and fingerprint[J]. IEEE Transactions on Information Forensics and Security,2018,13 (7):1810—1822.

YAO Z G,BARS J L,CHARRIER C,et al. Literature review of fingerprint quality assessment and its evaluation[J]. IET Biometrics, 2016,5(3):243—251.

SCHUCH P,SCHULZ S,BUSCH C. Survey on the impact of fingerprint image enhancement [J]. IET Biometrics,2018,7(2):102— 105.

SHEN L L,PENG K. No-reference stereoscopic image quality assessment based on wavelet-packet decomposition [J]. Journal of Hunan University(Nature Sciences),2018,45(10):139—147.(In Chinese)

ZHANG L,LI F,WU K T. Directionless triangle-matching fingerprint recognition[J]. Journal of Image and Graphics,2017,22(9): 1214—1221(. In Chinese)

RAONI F S T,NEUCIMAR J L. A new framework for quality assessment of high-resolution fingerprint images[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(10): 1905—1917.

ALI D,HADI K,SEYED M I,et al. Fingerprint distortion rectification using deep convolutional neural networks [C]//International Conference on Biometrics. Los Angeles,CA,USA: IEEE Biometrics Council,2018: 1—8.

JAVAD K,ALI M K. Fingerprint indexing based on minutiae pairs and convex core point [J]. Pattern Recognition,2017,67 (C): 110—126.

YAO Z,BARS J L,CHARRIER C,et al. Quality assessment of fingerprints with minutiae delaunay triangulation [C]//International Conference on Information Systems Security and Privacy. Angers, France: IEEE,2015: 315—321.

GALLALLY J,MARCEL S,FIERREZ J. Image quality assessment for fake biometric detection: application to iris,fingerprint and face recognition [J]. IEEE Transactions on Image Processing,2014,23(2): 710—724.

TARIQ M K,DONALD G B,MOHAMMAD A,et al. Efficient hardware implementation for fingerprint image enhancement using anisotropic gaussian filter [J]. IEEE Transactions on Image Processing,2017,26(5): 2116—2126.

MUBEEN G,SHAHZAIB I,SYED A T,et al. Efficient fingerprint matching using GPU [J]. IET Image Processing,2018,12 (2): 274—284.

GOK M,GORGUNOGLU S,ORAK I M. Fingerprint pre-processing on ARM and DSP platforms[J]. Electronics & Electrical Engineering,2014,20(6): 140—143.

LIU G C,WANG S Q. Multi-directional geometric nonlinear diffusion method for image denoising [J]. Journal of Hunan University (Nature Sciences),2016,43(8): 135—141.(In Chinese)


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