Objective

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Most automatic fingerprint identification systems (AFISs) are based on minutiae matching. The accuracy and reliability of minutiae extraction are largely dependent on the quality of the input fingerprint image. Thus

the performance of these AFISs is largely determined by the quality of input fingerprint images. In practice

the quality of a fingerprint image may suffer from various impairments

and the image may appear with ridge adhesions

ridge fractures

or uneven contrast. To improve the performance of AFISs

the quality of fingerprint images must be enhanced. This study proposes a novel fingerprint enhancement algorithm that uses sparse representation by multi-scale classification dictionaries.

Method

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First

we sample high-quality training fingerprints to build the training set for multi-scale classification dictionaries learning

and the multi-scale classification dictionaries are learned from the training set. A crucial issue in enhancing fingerprint images is obtaining an effective prior or constraint. Unlike generic images

fingerprint images have a steady and reliable ridge pattern. To obtain an effective prior or constraint

fingerprint patch orientations are estimated by weighted linear projection analysis (WLPA) on the basis of the vector set of point gradients. We classify training samples with the same size into eight groups according to their ridge orientation pattern. Instead of simply learning a dictionary

we learn a classification dictionary for each class with the same size. Second

fingerprints are pre-enhanced using the linear contrast stretching method. The sparse grey space in the fingerprint image is used

and the fingerprint image contrast can be stretched to cover the entire greyscale space. Consequently

the gray level information of the input fingerprint can be preserved against loss

and contrast enhancement can be improved. Contrast enhanced fingerprint contributes to the subsequent enhancement. Third

a fingerprint has a unique natural pattern

which is suitable for frequency-domain analysis. Generally

a good frequency-domain fingerprint enhancement approach is designed to work on spatial partitioning and frequency-domain enhancement. Thus

the fingerprint is partitioned into patches in the spatial domain on the basis of a non-overlapping window

the orientations of fingerprint patches are estimated by WLPA

and the qualities of the patches are evaluated and classified by the coherence of the point orientations. Finally

the fingerprint patches are transformed to the frequency domain by 2D discrete Fourier transform. The enhancement model of the patch spectrum is constructed via sparse representation modeling using classification dictionaries. The patch spectra are enhanced on the basis of a quality grading scheme and a composite strategy using multi-scale classification dictionaries learning combined with spectra diffusion. The fingerprint patch is enhanced according to its own priority

and patches with higher quality are enhanced when patches with lower quality are enhanced. Multi-scale classification dictionaries learning ensure the reliability of the enhancement. Spectra diffusion is successfully applied with the help of the quality grading and neighborhood priority scheme and the composite window strategy

and it can improve the quality of the patch spectra with low quality. Spectra diffusion provides accurate ridge spectra information for lower quality patches

thus ensuring the reliability of the ridge spectra for multi-scale classification dictionaries enhancing.

Result

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The proposed method is implemented and tested on fingerprint images from FVC2004. Some visual experiments and performance evaluations of minutiae extractions are illustrated. We compare our method with state-of-the-art fingerprint enhancement methods and report that the proposed method is superior in enhancing fingerprint images. Experimental results demonstrate that low-quality fingerprints can be effectively enhanced by the proposed method. Compared with traditional fingerprint enhancement algorithms

the proposed method is more robust against noise and exhibits a more prominent effect on low-quality fingerprint images.

Conclusion

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By introducing ridge pattern priori into a classification dictionary

a classification dictionary for each class with the same size is learned. Classification dictionaries based on the ridge pattern constraint can capture a reliable ridge pattern prior. Using classification dictionaries improves the effectiveness of the sparse modeling of information in a fingerprint patch. The quality grading scheme and the composite window strategy are adopted to assist the multi-scale dictionary in overcoming the contradiction between accuracy and anti-noise capability. Furthermore

the combination of composite window and quality evaluation ensures that spectra diffusion is successfully applied. The proposed method significantly improves the quality of low-quality input fingerprints.