Title
Fingerprint orientation field reconstruction by weighted discrete cosine transform
Abstract
Orientation field represents the topological structure of the interleaved ridge and valley flows in fingerprint images. Although a number of methods have been proposed for orientation estimation, reliable computation of orientation field is still a challenging problem due to the poor quality of some fingerprints. This paper proposes a method to reconstruct fingerprint orientation field by weighted discrete cosine transform (DCT). First, the DCT functions are used to build the basis atoms for linear representation of orientation field. Then, the DCT basis atoms of low and high orders are combined with the weights determined by singularity measurements for orientation reconstruction. The weighted DCT model is further extended for partial fingerprints to gradually and iteratively reconstruct the orientations in noisy or missing parts of fingerprints. The proposed method can perform well in smoothing out the noise while maintaining the orientation details in singular regions. Extensive experiments have been done to compare the proposed method with some existing methods on NIST and FVC fingerprint databases in terms of the reconstruction accuracy of orientation field, fingerprint indexing performance, and fingerprint recognition accuracy. Experimental results illustrate the effectiveness of the proposed method in reconstructing orientation fields, especially for poor quality and partial fingerprints.
Year
DOI
Venue
2014
10.1016/j.ins.2013.08.022
Inf. Sci.
Keywords
Field
DocType
poor quality,orientation estimation,fingerprint orientation field,fingerprint orientation field reconstruction,fingerprint image,weighted discrete cosine,orientation detail,orientation field,fvc fingerprint databases,partial fingerprint,orientation reconstruction,fingerprint recognition,discrete cosine transform
Fingerprint recognition,Linear representation,Discrete cosine transform,Singularity,Fingerprint,NIST,Smoothing,Artificial intelligence,Machine learning,Mathematics,Computation
Journal
Volume
ISSN
Citations 
268,
0020-0255
21
PageRank 
References 
Authors
0.62
30
3
Name
Order
Citations
PageRank
Manhua Liu132323.91
Shuxin Liu2294.17
Qijun Zhao341938.37