Gabor filters (GFs) play an important role in many application areas for the enhancement of various types of images and the extraction of Gabor features. For the purpose of enhancing curved structures in noisy images, we introduce curved GFs that locally adapt their shape to the direction of flow. These curved GFs enable the choice of filter parameters that increase the smoothing power without creating artifacts in the enhanced image. In this paper, curved GFs are applied to the curved ridge and valley structures of low-quality fingerprint images. First, we combine two orientation-field estimation methods in order to obtain a more robust estimation for very noisy images. Next, curved regions are constructed by following the respective local orientation. Subsequently, these curved regions are used for estimating the local ridge frequency. Finally, curved GFs are defined based on curved regions, and they apply the previously estimated orientations and ridge frequencies for the enhancement of low-quality fingerprint images. Experimental results on the FVC2004 databases show improvements of this approach in comparison with state-of-the-art enhancement methods.
Gabor filterbanks are used for the segmentation @cite_10 and quality estimation @cite_21 of fingerprint images, for core point estimation @cite_55 , classification @cite_56 and fingerprint matching based on Gabor features @cite_46 @cite_55 . GFs are also employed for generating synthetic fingerprints @cite_23 . The use of GF for fingerprint image enhancement was introduced in @cite_44 .
@cite_55 @cite_21 @cite_56 @cite_44 @cite_23 @cite_46 @cite_10
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Introduces a method for the generation of synthetic fingerprint images. Gabor-like space-variant filters are used for iteratively expanding an initially empty image containing just one or a few seeds. A directional image model, whose inputs are the number and location of the fingerprint cores and deltas, is used for tuning the filters according to the underlying ridge orientation. Very realistic fingerprint images are obtained after the final noising-and-rendering stage. We propose a Gabor-filter-based method for fingerprint recognition in this paper. The method makes use of Gabor filtering technologies and need only to do the core point detection before the feature extraction process without any other pre-processing steps such as smoothing, binarization, thinning, and minutiae detection. The proposed Gabor-filter-based features play a central role in the processes of fingerprint recognition, including local ridge orientation, core point detection, and feature extraction. Experimental results show that the recognition rate of the k-nearest neighbor classifier using the proposed features is 97.2 for a small-scale fingerprint database, and thus that the proposed method is an efficient and reliable approach. Fingerprint image enhancement is a crucial step in automatic fingerprint recognition. A large number of approaches for filtering the fingerprint image have been suggested. Most of them perform oriented band pass filtering. However, such filters, for example, Gabor filters, may create spurious ridge structure information. This is harmful for the feature extraction and therefore is harmful for the automatic fingerprint recognition. This paper examines the properties of applying the Gabor filter in the fingerprint image enhancement. It shows that the nonsinusoidal-shaped ridge structure, the ridge frequency estimation error, and the small filter size are the causes of creating spurious ridge structures. As a solution, we suggest an adaptive oriented low pass filter instead of the Gabor filter to avoid producing undesired harmful side effects for the automatic fingerprint recognition. An important step in fingerprint recognition is the segmentation of the region of interest. In this paper, we present an enhanced approach for fingerprint segmentation based on the response of eight oriented Gabor filters. The performance of the algorithm has been evaluated in terms of decision error trade-off curves of an overall verification system. Experimental results demonstrate the robustness of the proposed method.