FINGERPRINT ENHANCEMENT BASED ON WAVELET AND ANISOTROPIC FILTERING

BackgroundOrientation field (OF) plays a very significant role in automatic fingerprint recognition systems. Many algorithms have been proposed for the estimation of fingerprints’ OF but it is hard to solve the dilemma of correcting spurious ridge structure and avoiding singularity location deviation, especially for poor images. So far, the following drawbacks still need to be solved for OF construction methods for practical application: (1) How to adaptively choose block scales to resolve the contradiction between accuracy and anti-noise, since small scale is beneficial to accuracy but is sensitive to noise, while large scale is more resistant to noise, but the accuracy is deteriorated. (2) How to construct the genuine OF in the areas close-by singular points and to evade singularity location deviation? Current block based methods give spurious OF estimates in the area near singular points because these areas have large curvature thus the detected singular points deviate from the genuine localizations. When these singular points are used as the anchor for referencing minutiae, it makes the average error of matching or recognition even larger. Therefore, it is essentials to construct the genuine OF in the areas close-by singular points and to evade singularity deviation.MethodsTo overcome the above-mentioned limitations, a novel method, combining a weighted multi-scale composite window (WMCM) with a hierarchical smoothing strategy has been proposed for the computation of fingerprint OF. This method mainly contains two procedures: the approximate OF estimation and the hierarchical OF smoothing. In the first procedure, a series of OFs are established under multiple scales of composite windows by using a gradient based method then a coarse OF is estimated using the weight of each scale determined by a squared gradient consistency. In the second procedure, the OF is first quantized into a two-digitized orientation zone and a two-orientation-zone filtering strategy is adapted to the OF blocks based on a filtering mask obtained after eliminating the isolated blocks. In the end a similar three-digitized orientation zone is performed to obtain an accurate and smooth OF. To validate the performance, the proposed method has been applied to OF computation using the FVC2004 databases and three experiments are designed. Experiment 1 aims to validate whether the weighted multi-scale composite window can balance the dilemma of accuracy and robustness more effectively than the previous works do. Experiment 2 is designed to examine whether the hierarchical smoothing method can correct the spurious ridge flow and preserve the genuine localization of singular points. The purpose of experiment 3 is to test the performance of the proposed method on OF reconstruction in low quality fingerprint images. The fingerprint databases FVC 2004 DB1–DB4 are employed in this study.ResultsThe results of experiment I shows that the proposed method is capable to extract the information of OF reliably and it is more robust against singularity localization deviation in comparison with the other three gradient based methods. The results of experiment II indicates that the proposed smoothing method can balance the contradiction in correcting spurious ridge structures and preserving genuine singularity localization. The results of experiment III illustrates that our approach combing WMCW with the hierarchical smoothing method is capable to extract the information of OF ridge reliably and it is more robust against singularity deviation in comparison with the other three gradient based methods. In a word, the experiment results demonstrate that the proposed method can correct spurious ridge structure and meanwhile avoid singularity deviation compared with the previous works.ConclusionsA novel gradient based algorithm has been proposed which is more reliable for the estimation of the ridge information for fingerprint OF and is more accurate in preserving the singularity localization. Compared with the previously proposed gradient based methods, the advantages of the proposed RBSF lie in three aspects. Firstly a weighted multi-scale composite window is put forward to replace the single window used by conventional gradient based methods and to adaptively choose the scales of the blocks. Secondly, a hierarchical smoothing strategy is proposed to enhance the OF by using the two-orientation-zone filtering and the three-orientation-zone filtering, aiming to correct the spurious ridges and preserving the genuine location of singular points. Finally, three experiments are designed to test the proposed algorithm together with other popular gradient based methods on real fingerprint images, which are selected from different categories and all are suffering from obvious noise effects. All the experiment results show that the proposed method is superior with respect to reliable OF construction and avoiding singularity localization deviation.

The quality of fingerprint images strongly affects any fingerprint biometric system. In order to deal with low quality fingerprint images and overcome the limits of traditional enhancement techniques, a cuckoo-search based algorithm for fingerprint image contrast enhancement is proposed in this paper. The algorithm combines the use of cuckoo search for both the gray level mapping technique for contrast enhancement, and a new objective function as a quality metric for global fingerprint image enhancement. The enhancement scheme is assessed on low quality images from a reference database, the FVC 2000. The obtained results show that the proposed cuckoo algorithm can qualitatively and quantitatively enhance fingerprint images, on the general level of both noise eradication and quality metrics. In addition, the proposed algorithm can visually and numerically clarify the fingerprints ridge structure, thus, the minutiae detection process in comparison with the use of a traditional enhancer. Hence, the proposed algorithm has proven to be very efficient for fingerprint image quality enhancement for easy further processing.

To extract singular points from low quality fingerprint image, a two-phase algorithm for singular points extracting is presented in this paper. In the first phase, an improved Poincare index is used to extract candidate singularities. In the second phase, Gaussian-Hermite moment attribution of ridge for each candidate is calculated to determine its authenticity. Because of effectively assembling information of ridge orientation and coherence in singularitypsilas neighborhood, this method can extract singularities in a relatively accurate and reliable way. Experiments have shown its effectiveness and robustness.

An algorithm is proposed, which combines global and local information of fingerprint images to detect singular points. It’s mathematically proven that normal lines of gradient of double orientation field(GDOF) pass through singular points. Normal lines of GDOF use rather global information to detect candidate singular points. Fingerprint image is divided into blocks and normal lines of GDOF are drawn. The number of normal lines that pass through each block is accumulated. The block that has the maximum number corresponds to a candidate singular point. It can be seen as a kind of Hough transform(HT). As candidate singular points detected by global information may have a little warp from their real positions, it’s necessary to use local information to refine their positions. Poincare index is chosen, and uses local information to refine the candidate singular points. This makes our algorithm more robust to noise than methods that only use local information. What’s more, the pairs of the detected singular points are used to classify fingerprint. Experimental results show that our algorithm performs well and fast enough for real time application in databases NIST-4.

Fingerprint classification is used to assign fingerprints into five established classes, namely, Whorl, Left loop, Right loop, Arch, and Tented Arch, on the basis of ridge structures and singular points’ trait. Although some progresses have been achieved in improving accuracy rates, problems arise from ambiguous fingerprints, especially those with large intraclass and small interclass variations. Poor-quality images, such as those with blur, dry, wet, low contrast, cut, scarred and smudgy features, are equally challenging. This study proposes a new classification technique based on template matching by using fingerprint salient features as a matching tool. In classification phase, a new set of fingerprint features is created based on singular points’ occurrence and location along the symmetric axis. A set of five templates, in which each template represents a specific true class, is then generated. Finally, classification is performed by calculating similarity between the query fingerprint image and the template images by using ×2 distance measure. The performance of the current method is evaluated in terms of accuracy by using 27,000 fingerprint images acquired from the National Institute of Standard and Technology Special Database 14, which is a de facto dataset for development and testing of fingerprint classification systems. Experimental results have a high accuracy rate of 93.05%.

Fingerprint ridge orientation field reconstruction using the best quadratic approximation by orthogonal polynomials in two discrete variables

A fingerprint image is composed of a foreground and background areas. The foreground area is composed of ridges and valleys, while the background area is the region that surrounds the fingertip and is mainly noisy. Fingerprint recognition techniques amount to extracting minutiae as well as identifying ridge and valley regions. As the captured fingerprint images may be of poor quality due to various reasons, it is necessary to enhance the image prior to minutiae extraction. Usually, fingerprint images are enhanced either in the spatial or frequency domains. However, due to the complicated ridge structures, the quality of the enhanced images is not satisfactory. In this paper, a novel enhancement method is proposed. It makes use of a recently developed fingerprint segmentation technique, in order to separate the fingerprint foreground and background areas, and subsequently speeds up the enhancement process. Next, a novel interpolation-based technique is proposed to join broken ridges. Finally, in order to estimate the ridge-valley spatial spacing, an accurate Radon-based technique is proposed. This accurate spacing is necessary for the design of 2-D directional Gabor filter that removes artifacts, and isolated black regions. The enhancement algorithm has been implemented and tested on fingerprint images drawn from FVC2002 and FVC2004 databases. A qualitative measure of the enhanced performance is also described. Simulation results show that our proposed algorithm has verified significant quality improvement over the existing published techniques.

On account of restriction of man-made and collection environment, the fingerprint image generally has low quality, especially a contaminated background. In this paper, an enhancement algorithm based on edge filter and Gabor filter is proposed to solve this kind of fingerprint image. Firstly, a gray-based algorithm is used to enhance the edge and segment the image. Then, a multilevel block size method is used to extract the orientation field from segmented fingerprint image. Finally, Gabor filter is used to fulfill the enhancement of the fingerprint image. The experiment results show that the proposed enhancement algorithm is effective than the normal Gabor filter algorithm. The fingerprint image enhance by our algorithm has better enhancement effect, so it is helpful for the subsequent research, such as classification, feature exaction and identification.

One of the most difficult problems in fingerprint recognition has been that the recognition performance is significantly influenced by fingertip surface difficult conditions (e.g., rough fingertips, dry fingertips, allergic-skin fingertips), which may vary depending on environmental or personal causes. Addressing this problem, this paper presents the three-rate hybrid Kohonen neural network (TRHKNN) for low-quality fingerprint image processing. The received TRHKNN consists of "fast" Kohonen neural network, "middle" Kohonen neural network and "slow" Kohonen neural network. The illustrative example - recognition and restoration of distorted low-quality fingerprint - was carried out using this TRHKNN. The received TRHKNN has not only high speed of image recognition, but also high speed of image restoration. The simulations for the TRHKNN were carried out in a MATLAB/Simulink environment. This example demonstrates an efficient recognition performance of the proposed computing procedure and applicability of the TRHKNN for fast-acting low-quality image recognition in real-time conditions.

Fingerprint images are direction-oriented pattern formed by ridges and valleys. The dominant direction of ridge plays a very important role in the enhancement of fingerprints. This paper presents a new method of decimation-free directional filter banks (DFB)-based analysis for fingerprint enhancement. Its analysis outputs are shift-invariant directional images but not directional sub-band images in previous DFBs. The algorithm first decomposes a fingerprint image into eight directional images in the analysis stage; then obtains the orientation image based on directional energy distributions for each block and processes the directional images based on the orientation image in the processing stage; at last reconstructs them to the enhanced image in the synthesis stage. Experimental results show that the proposed method reduces the influence of noise, links the injured ridges and enhances the ridges' moving shape.

Fingerprint identification is one of the most popular and efficient biometric techniques used for improving automatic personal identification. In this paper, we will present a new indexing method, based on estimation of singular point considered as an important feature in the fingerprint by using the directional file. On the other hand, a hierarchical Delaunay triangulation is applied on the minutiae around the extracted singular point. Two fingerprints calculated by introducing the barycenter notion to ensure the exact location of the similar triangles is compared. We have performed extensive experiments and comparisons to demonstrate the effectiveness of the proposed approach using a challenging public database (i.e., FVC2000), which contains small area and low-quality fingerprints.

Fingerprint enhancement rooted in the spectra diffusion by the aid of the 2D adaptive Chebyshev band-pass filter with orientation-selective

Fingerprint image preprocessing technology has been widely used with the development of intelligent identity recognition system, which studies fingerprint image restoration, enhancement and thinning based on digital image processing technology. This paper presents system design and realization of preprocessing technology for fingerprint image. Fingerprint image has restoration processing by adopting image filed calculation, image background segmentation, image equalization, image convergence and image smoothing algorithm in order at first. Then Gabor wavelet algorithm is used to enhance the contrast of fingerprint image. Image thinning algorithm is carried out to extract the skeleton of fingerprint image at last. Test result shows that the method presented has good accuracy, quick speed and strong robustness for realtime application.

Fingerprints are one of the most commonly used biometrics in public safety. The quality of the fingerprint image directly affects the performance of the fingerprint recognition system. This paper proposes a low-quality fingerprint enhancement model based on U-Net, which can effectively remove the background noise of the fingerprint image and repair the damaged ridge structure of the fingerprint. Furthermore, to train the proposed model, we create a dataset containing low-quality fingerprints and their associated ground truth. We compare the proposed method with several state-of-the-art methods on the home-made dataset and the public dataset FVC2002, and the experimental results demonstrate that the proposed method performs better.

As one of the most important and obvious global features for fingerprints, the singular point plays an essential role in fingerprint registration and fingerprint classification. To date, the singular point detection methods in the literature can be generally divided into two categories: methods based on traditional digital image processing and those on deep learning. Generally speaking, the former requires a high-precision fingerprint orientation field for singular point detection, while the latter just needs the original fingerprint image without preprocessing. Unfortunately, detection rates of these existing methods, either of the two categories above, are still unsatisfactory, especially for the low-quality fingerprint. Therefore, regarding singular point detection as a semantic segmentation of the small singular point area completely and directly, we propose a new customized convolutional neural network called SinNet for segmenting the accurate singular point area, followed by a simple and fast post-processing to locate the singular points quickly. The performance evaluation conducted on the publicly Singular Points Detection Competition 2010 (SPD2010) dataset confirms that the proposed method works best from the perspective of overall indexes. Especially, compared with the state-of-art algorithms, our proposal achieves an increase of 10% in the percentage of correctly detected fingerprints and more than 16% in the core detection rate.