Abstract
In order to improve the quality of fingerprint with a large noise, this study proposes a fingerprint enhancement method by using a sparse representation of learned multi-scale classification dictionaries with reduced dimensionality. The multi-scale dictionary is used to balance the contradiction between the accuracy and the anti-noise ability, which is an ideal solution to reconcile the demands of enhancement quality and computational performance. The principal component analysis is applied in the authors’ technique for dimension reduction of multi-scale classification dictionaries. Under the quality grading scheme and multi-scale composite windows, the fingerprint patches are enhanced by using a sparse representation of learned multi-scale classification dictionaries with reduced dimensionality according to their priorities. In addition, the multi-scale composite windows help the more high-quality spectra diffuse into the low-quality fingerprint patches and this can greatly improve the spectra quality of them. Experimental results and comparisons on FVC 2000 and FVC 2004 databases are reported. And it shows that the proposed method yields better results in terms of the robustness of fingerprint enhancement as compared with the latest techniques. Moreover, the results show that the proposed algorithm can obtain better identification performance.
Highlights
Fingerprints are the most common biometrics used for personal identification in commercial and forensic areas [1,2,3,4]
To validate our proposed method on fingerprint enhancement performance, the performance of proposed algorithm is evaluation based on the public competition fingerprint databases FVC 2000 and FVC 2004
The comparative experimental results demonstrate that the proposed method is more effective and efficient in fingerprint image enhancement than the existing methods such as Gabor filter method [6], Chikkerur’s short time Fourier transform (STFT) method [15], Sutthiwichaiporn’s adaptive boosted spectral filtering (ABSF) method [16], Ding’s adaptive Chebyshev band-pass filter (ACBF) [17] method and Ding’s classification dictionaries learning (CDL) method [31]
Summary
Fingerprints are the most common biometrics used for personal identification in commercial and forensic areas [1,2,3,4]. In view of this, Ding et al [17] designed a robust 2D adaptive Chebyshev band-pass filter (ACBF) with orientation-selectivity to enhance fingerprint based on the quality grading scheme It succeeded with the aid of spectra diffusion. In order to overcome the limitations of the Gabor filter, Ding et al [31] used a sparse representation-based classification dictionaries learning (CDL) to enhance the fingerprint image. Instead of learning a shared sparse representation for all fingerprint patches, the proposed method learn classification dictionaries for each class training dataset by using dimension-reduced dictionaries achieved by the Principal Component Analysis (PCA). It can better capture the intrinsic ridge pattern prior.
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