FVC2002 Databases Research Articles

Sensor-oriented feature usability evaluation in fingerprint segmentation

Existing fingerprint segmentation methods usually process fingerprint images captured by different sensors with the same feature or feature set. We propose to improve the fingerprint segmentation result in view of an important fact that images from different sensors have different characteristics for segmentation. Feature usability evaluation, which means to evaluate the usability of features to find the personalized feature or feature set for different sensors to improve the performance of segmentation. The need for feature usability evaluation for fingerprint segmentation is raised and analyzed as a new issue. To address this issue, we present a decision-tree-based feature-usability evaluation method, which utilizes a C4.5 decision tree algorithm to evaluate and pick the best suitable feature or feature set for fingerprint segmentation from a typical candidate feature set. We apply the novel method on the FVC2002 database of fingerprint images, which are acquired by four different respective sensors and technologies. Experimental results show that the accuracy of segmentation is improved, and time consumption for feature extraction is dramatically reduced with selected feature(s).

Fast and accurate algorithm for core point detection in fingerprint images

The core point is used to align between the fingerprints in the fingerprint authentication systems faster than the conventional techniques. To speed up the processing for the real time applications, it is more convenient to implement the image processing algorithms using embedded modules that can be used in the portable systems. To do this, the algorithm should be characterized by a simple design for easier and more feasible implementation on the embedded modules. The proposed work, in this paper, presents a mask that locates the core point simply from the ridge orientation map. The introduced algorithm detects the core point at the end of the discontinuous line appearing in the orientation map presented by a gray-scale. A property is presented and supported with a mathematical proof to verify that the singular regions are located at the end of this discontinuous line. The experimental results, on the public FVC2002 and FVC2004 databases, show that the proposed mask exhibits an average increase in the correct core point detection per fingerprint by 17.35%, with a reduction in the false detection by 51.23%, compared to a fast edge-map based method. Moreover, the execution time is reduced by an average factor of 1.8.

ISO/IEC standards for on-card biometric comparison

On-card biometric comparison is getting more attention from government and the IT industry because of the higher level of security and its ability to prevent the enrolment template from leakage that can cause privacy concern. To address the interoperability needs, the ISO/IEC SC17 WG11 published a standard entitled ISO/IEC 24787 - 'On-card biometric comparison'. This paper will give an introduction to this new standard and analyse the limitation of implementing minutiae matching algorithm using this standard on low-cost smartcard. We will present a novel algorithm to perform off-card template alignment using the work-sharing mechanism. The final matching process is executed on-card to ensure that the security is not compromised. The average verification time of our proposed algorithm is about 2.5 seconds with an 8-bit Java card with an average EER <= 4.3% using FVC2000 and FVC2002 databases. Hence, the performance is comparable to those minutiae matching algorithms running on PC.

Impact of Algorithms for the Extraction of Minutiae Points in Fingerprint Biometrics

Problem statement: As the importance of automatic personal identificat ion applications increases, biometrics particularly fin gerprint identification is the most consistent and greatly acknowledged technique. A very important st ep in automatic fingerprint recognition system is to extract the minutiae points from the i nput fingerprint images automatically and quickly. Approach: Fingerprints from the database FVC2002 (DB1-a) is used for experimental purpose. The minutiae points from 100 fingerprints were detected. It is proposed to use Minutiae Detection using Crossing Numbers (MDCN) and Minutiae Detection using Midpoint Ridge Contour Method (MDMRCM). Finally the performance of minutiae extraction algorithms using the number of minutiae detected in both the cases w ere analysed. Results: The result shows that the avearge performance of MDCN method for minutiae points detection is 88% and for MDMRCM method is 92%. Conclusion: The performance of MDMRCM is better than MDCN method. MDMRCM method extract more minutiae points than MDCN method. It consumes lesser time to get the output and the false minutiae points were not d etected. And hence MDMRCM method is considered to be a superior than MDCN method.

ROTATED WAVELET FILTERS-BASED FINGERPRINT RECOGNITION

A novel approach for feature extraction of fingerprint matching is proposed by using two-dimensional (2D) rotated wavelet filters (RWF). 2D RWF are used to capture the characterization of diagonally oriented information present in fingerprint image. Proposed method extracts the significant information from small area of fingerprint image. Experimental results conducted on standard database of Bologna University and FVC2002 indicate that the proposed method improves the genuine acceptance rate (GAR) from 92.14% to 96.12% and reduces false acceptance rate (FAR) from 25.2% to 21.2% on Bologna University database and it reduces FAR from 36.71% to 22.79% on FVC2002 database compared with discrete wavelet transform-based approach.

Hartley Transform Based Fingerprint Matching

The Hartley transform based feature extraction method is proposed for fingerprint matching. Hartley transform is applied on a smaller region that has been cropped around the core point. The performance of this proposed method is evaluated based on the standard database of Bologna University and the database of the FVC2002. We used the city block distance to compute the similarity between the test fingerprint and database fingerprint image. The results obtained are compared with the discrete wavelet transform (DWT) based method. The experimental results show that, the proposed method reduces the false acceptance rate (FAR) from 21.48% to 16.74 % based on the database of Bologna University and from 31.29% to 28.69% based on the FVC2002 database.

Improving Fingerprint Verification Using Minutiae Triplets

Improving fingerprint matching algorithms is an active and important research area in fingerprint recognition. Algorithms based on minutia triplets, an important matcher family, present some drawbacks that impact their accuracy, such as dependency to the order of minutiae in the feature, insensitivity to the reflection of minutiae triplets, and insensitivity to the directions of the minutiae relative to the sides of the triangle. To alleviate these drawbacks, we introduce in this paper a novel fingerprint matching algorithm, named M3gl. This algorithm contains three components: a new feature representation containing clockwise-arranged minutiae without a central minutia, a new similarity measure that shifts the triplets to find the best minutiae correspondence, and a global matching procedure that selects the alignment by maximizing the amount of global matching minutiae. To make M3gl faster, it includes some optimizations to discard non-matching minutia triplets without comparing the whole representation. In comparison with six verification algorithms, M3gl achieves the highest accuracy in the lowest matching time, using FVC2002 and FVC2004 databases.

FINGERPRINT ENHANCEMENT BASED ON WAVELET AND ANISOTROPIC FILTERING

The importance of high-fidelity enhancement in low quality fingerprint image cannot be overemphasized. Most of the existing fingerprint enhancement methods are contextual filter-based methods and they often suffer from two shortcomings: (1) there is block effect on the enhanced images; and (2) they blur or destroy ridge structures around singular points. In order to well preserve the ridge structures in singular regions and avoid block effect, we develop a new method for fingerprint enhancement combining nontensor product wavelet filter banks and anisotropic filter. We first decompose the fingerprint image using the nontensor product wavelet filter banks. Then we modify the approximation subimage using anisotropic filtering and adjust the high frequency coefficients of the three other subimages by applying the adaptive approach to reduce the noises according to the geometry feature of images. Finally, the inverse transform is applied to map the result and a final contrast enhancement is done subsequently. Experiments have been conducted on the fingerprint database FVC2004 in our study. The results demonstrate that the proposed approach is capable of overcoming block effect and enhancing low quality fingerprint while preserving the ridge structures around singular points.

A framework of multitemplate ensemble for fingerprint verification

How to improve performance of an automatic fingerprint verification system (AFVS) is always a big challenge in biometric verification field. Recently, it becomes popular to improve the performance of AFVS using ensemble learning approach to fuse related information of fingerprints. In this article, we propose a novel framework of fingerprint verification which is based on the multitemplate ensemble method. This framework is consisted of three stages. In the first stage, enrollment stage, we adopt an effective template selection method to select those fingerprints which best represent a finger, and then, a polyhedron is created by the matching results of multiple template fingerprints and a virtual centroid of the polyhedron is given. In the second stage, verification stage, we measure the distance between the centroid of the polyhedron and a query image. In the final stage, a fusion rule is used to choose a proper distance from a distance set. The experimental results on the FVC2004 database prove the improvement on the effectiveness of the new framework in fingerprint verification. With a minutiae-based matching method, the average EER of four databases in FVC2004 drops from 10.85 to 0.88, and with a ridge-based matching method, the average EER of these four databases also decreases from 14.58 to 2.51.

Oriented diffusion filtering for enhancing low-quality fingerprint images

To enhance low-quality fingerprint images, we present a novel method that first estimates the local orientation of the fingerprint ridge and valley flow and next performs oriented diffusion filtering, followed by a locally adaptive contrast enhancement step. By applying the authors’ new approach to low-quality images of the FVC2004 fingerprint databases, the authors are able to show its competitiveness with other state-of-the-art enhancement methods for fingerprints like curved Gabor filtering. A major advantage of oriented diffusion filtering over those is its computational efficiency. Combining oriented diffusion filtering with curved Gabor filters led to additional improvements and, to the best of the authors’ knowledge, the lowest equal error rates achieved so far using MINDTCT and BOZORTH3 on the FVC2004 databases. The recognition performance and the computational efficiency of the method suggest to include oriented diffusion filtering as a standard image enhancement add-on module for real-time fingerprint recognition systems. In order to facilitate the reproduction of these results, an implementation of the oriented diffusion filtering for Matlab and GNU Octave is made available for download.

A Novel Probabilistic Model Based Fingerprint Recognition Algorithm

A novel fingerprint recognition algorithm based on the probabilistic graphical model is proposed in this paper. First, minutiae in query fingerprint are viewed as random variables with the minutiae in template print as the realizations. According to the random variables, a 2-tree model is built by selecting two signal points from the query set. Second, the model is converted into a Junction Tree, and the potentials of the tree nodes are defined according to the intrinsic characters of fingerprint. After that, Junction Tree (J.T.) algorithm is performed to obtain the correspondence of the two sets of minutiae. To deal with many-to-one corresponding problem caused by the outliers, we repeat the process by exchanging two sets. Finally, the similarity of the two fingerprints is evaluated using the number of common matching pairs and the maximal posteriori probability generated by the J.T. algorithm. Experiments performed on databases of FVC2004 achieve the perfect performance.

Fingerprint Matching Based on Orientation Feature

This paper presents a fast and reliable algorithm for fingerprint verification. Our proposed fingerprint verification algorithm is based on image-based fingerprint matching. The improved orientation feature vector of two fingerprints has been compared to compute the similarities at a given threshold. Fingerprint image has been aligned by rotating through an angle before feature vector is computed and matched. Row and Column variance feature vector of orientation image will be employed. The algorithm has been tested on the FVC2002 Databases. The performance of algorithm is measured in terms of GAR and FAR. At a threshold level of 1.1 % and at 5.7% FAR the GAR observed is 97.83%. The improved Feature vector will lower imposter acceptance rate at reasonable GAR and hence yields better GAR at lower FAR. The proposed algorithm is computationally very efficient and can be implemented on Real-Time Systems.

Curved-Region-Based Ridge Frequency Estimation and Curved Gabor Filters for Fingerprint Image Enhancement

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.

Ultrafast fingerprint indexing for embedded systems

A novel core-based fingerprint indexing scheme for embedded systems is presented in this paper. Our approach is enabled by our new precise and fast core-detection algorithm with the direction map. It introduces the feature of CMP (core minutiae pair), which describes the coordinates of minutiae and the direction of ridges associated with the minutiae based on the uniquely defined core coordinates. Since each CMP is identical against the shift and rotation of the fingerprint image, the CMP comparison between a template and an input image can be performed without any alignment. The proposed indexing algorithm based on CMP is suitable for embedded systems because the tremendous speed up and the memory reduction are achieved. In fact, the experiments with the fingerprint database FVC2002 show that its speed for the identifications becomes about 40 times faster than conventional approaches, even though the database includes fingerprints with no core.

A method using long digital straight segments for fingerprint recognition

In this paper, we proposed a new method using long digital straight segments (LDSSs) for fingerprint recognition based on such a discovery that LDSSs in fingerprints can accurately characterize the global structure of fingerprints. Different from the estimation of orientation using the slope of the straight segments, the length of LDSSs provides a measure for stability of the estimated orientation. In addition, each digital straight segment can be represented by four parameters: x-coordinate, y-coordinate, slope and length. As a result, only about 600 bytes are needed to store all the parameters of LDSSs of a fingerprint, as is much less than the storage orientation field needs. Finally, the LDSSs can well capture the structural information of local regions. Consequently, LDSSs are more feasible to apply to the matching process than orientation fields. The experiments conducted on fingerprint databases FVC2002 DB3a and DB4a show that our method is effective.

Fingerprint Matching Incorporating Ridge Features With Minutiae

This paper introduces a novel fingerprint matching algorithm using both ridge features and the conventional minutiae feature to increase the recognition performance against nonlinear deformation in fingerprints. The proposed ridge features are composed of four elements: ridge count, ridge length, ridge curvature direction, and ridge type. These ridge features have some advantages in that they can represent the topology information in entire ridge patterns existing between two minutiae and are not changed by nonlinear deformation of the finger. For extracting ridge features, we also define the ridge-based coordinate system in a skeletonized image. With the proposed ridge features and conventional minutiae features (minutiae type, orientation, and position), we propose a novel matching scheme using a breadth-first search to detect the matched minutiae pairs incrementally. Following that, the maximum score is computed and used as the final matching score of two fingerprints. Experiments were conducted for the FVC2002 and FVC2004 databases to compare the proposed method with the conventional minutiae-based method. The proposed method achieved higher matching scores. Thus, we conclude that the proposed ridge feature gives additional information for fingerprint matching with little increment in template size and can be used in conjunction with existing minutiae features to increase the accuracy and robustness of fingerprint recognition systems.

Fingerprint Image Enhancement Algorithm Based on FDCT

Fingerprint enhancement is an essential preprocessing step and it is crucial for the efficiency of fingerprint recognition algorithm. We present an enhancement algorithm based on fast discrete curvelet transform (FDCT). First, implement positive transform on input image, namely decompose the image into coarse scales and fine scales coefficients. Then make use of a directional filter and a soft threshold function to enhance image and reduce noise respectively. Finally, implement inverse transform, and reconstruct the enhanced image. Experiments are carried out on FVC2004 databases. For bad quality fingerprints, the results indicate that the proposed algorithm has better enhancement and de-noising effect than traditional methods, and need less time.

Fast Matching Algorithm Based on Fingerprint Classification Information

This paper focuses on fingerprint minutia matching algorithm. A special minutia neighbor structure is proposed during the matching process in this algorithm. It can locate fingerprints using the singular from classification information. In addition, minutia structure can be used to save the time of matching minutia in a simple but effective way. Then, the matching of minutia is based on the changeable sized boundary box. At the same time, possible reference position is computed to make sure the algorithm more robust to nonlinear deformation from fingerprint images. Experimental results on Fingerprint verification competition FVC2004 databases show that this algorithm can speed up the matching of fingerprint database with a preferable performance.

Denoising of Fingerprint Images using Q-shift Complex Wavelet Transform

In this paper, we present a novel image denoising method using Q-shift with Dual Tree Complex Wavelet Transform (QDTCWT) for denoising fingerprint images. The DTCWT is an over complete wavelet transform with limited redundancy and generates complex coefficients in parallel using a dual tree of wavelet filters. But, low pass delay produces a Hilbert pair relationship between two trees. This is well addressed by Q-shift filters for improving orthogonality and symmetry properties in level 2 and below. QDTCWT have features like linear phase, tight frame, compact spatial support, good frequency domain selectivity with low sidelobe levels, approximate shift invariance, and good directional selectivity in two or more dimensions. This provides the QDTCWT basis mainly useful for de-noising purposes with high degree of shift-invariance and better directionality compared to the other traditional methods. The proposed algorithm has been designed in the MATLAB TM environment and tested in the fingerprint images obtained from the FVC2004 database for denoising. The performance and efficiency of the algorithm are estimated by calculating various quality metrics and compared with the advanced methods already practiced in fingerprint image denoising. The results of this study revealed that the QDTCWT algorithm is capable of producing high quality finger print images with greater fidelity, high robustness and accuracy over the other traditional denoising methods.

Multiresolution Feature Based Subspace Analysis for Fingerprint Recognition

The image intensity surface in an ideal fingerprint image contains a limited range of spatial frequencies, and mutually distinct textures differ significantly in their dominant frequencies. This paper presents a multiresolution feature based subspace technique for fingerprint recognition. The technique computes the core point of fingerprint and crops the image to predefined size. The multiresolution features of aligned fingerprint are computed using 2-D discrete wavelet transform. LL component in wavelet decomposition is concatenated to form the fingerprint feature. Principal component analysis is performed on these features to extract the features with reduced dimensionality. The algorithm is effective and efficient in extracting the features. It is also robust to noise. Experimental results using the FVC2002 and Bologna databases show the feasibility of the proposed method..