Combining multi-scale composite windows with hierarchical smoothing strategy for fingerprint orientation field computation

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.

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.

Reference point identification is important in automatic fingerprint recognition system as it can be used to align fingerprints in a correct orientation in spite of the possibility of different transformations in fingerprint images. It is also used in fingerprint classification, as it is desirable to classify fingerprint images for forensic type applications which require the input image to be verified against a large database. The important feature information useful for classification is centered near the reference point. Most of the current approaches for identifying the reference point either require determining ridge orientation or use some complex filters. These methods either operate on 2D (two dimensional) or are not robust to rotation or cannot be applied to every class of fingerprint image. This paper proposes a method to reliably identify unique reference point that operates in 1D (one dimensional). The method treats the fingerprint ridges as a non-overlapped sequence of chain code segments. A modified k-curvature method has been proposed to find the high-curvature area of fingerprint ridges. The reference point localization is based on the property of the ridge's bending energy. The proposed method is tested on FVC2002 and FVC2004 standard datasets, and the experimental results show that the proposed algorithm can accurately locate reference point for all types of fingerprint images.

Adaptive fingerprint pore modeling and extraction

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.

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.

A novel approach for segmentation of good, normal and bad quality fingerprint image is proposed in this paper. Which is different from traditional methods that usually use certain features to segment diversified image. This work is divided in three steps: firstly, we enhance the quality of image to remove great many false traces effectively and got a uniform background. Secondly, we have extracted the background of ridge area and finally; we segment the foreground from background. Our results shows that the proposed technique successfully segments the good, normal and bad quality fingerprint images and also enhance the quality of image. We have evaluated the performance of the segmented image through objective and subjective quality measures like mean, standard deviation and Fourier spectrum. We have tested this technique for 3200 images taken from database FVC2002. Our technique yields better results as compared to traditional segmentation techniques. The results that we have achieved are more useful in automatic fingerprint recognition system

Fingerprint segmentation is a crucial and important step of image processing in automatic fingerprint identification. Because, it is very important for alright fingerprint features extraction, such as, singular points, bifurcation and ridge ending minutia’s. The aim of the segmentation of fingerprint is to extract the interest area (foreground) and to exclude the background regions, in order to reduce the time of subsequent processing and to avoid detecting false features. This paper presents a new approach of fingerprints segmentation. This approach is based on variance image and combined fuzzy C-mean algorithm with the statistical features. Fingerprint segmentation results from the proposed method are validated and the accuracy of segmentation sensitivity for the test data available is evaluated. We have tested this technique on more than 1000 images fingerprint taken from “CASIA Fingerprint Image Database Version 5.0” (CASIA-FingerprintV5). Then a comparative study with the existing techniques is presented. The experimental results demonstrate the superiority, the effectiveness and the robustness of the proposed method. Key words: Fuzzy C-means, biometrics, fingerprint image, segmentation, statistical features.

AFIS (automated fingerprint identification system) is very popular now days for biometric security. Fingerprint classification plays a key role in identifying fingerprints and also helps in fingerprint matching. This paper presents a new classification technique based on the detection of singular points (core and delta points). This fingerprint classification technique consists of four steps. In the first step, preprocessing (segmentation and normalization) of input fingerprint image is done. In the second step, fine orientation field of image is estimated. In the third step, singular points are located using modified Poincare index technique. In the fourth step, classification is done on the basis of singular points. The proposed technique was tested on FVC2004 database and the results show a significant improvement in reducing the misclassification errors.

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%.

In the process of mechanism design and trajectory planning of in-parallel actuated manipulators, it is necessary to know the locations of all singular points in the working space. We propose a new method for numerically locating the singular points of in-parallel actuated manipulators based on the pressure angle. A singular-point curve is defined as a set of singular points. Kinematic and static characteristics at singular points are analyzed by pressure angles and instantaneous screw axes. Singular points of the Stewart Platform are investigated using the proposed method. The neighborhood of singular points is determined from output error, isotropy of output force and driving force, and thus the practical working space is defined.

High-resolution automated fingerprint recognition systems (AFRSs) offer higher security because they are able to make use of level-3 features, such as pores, that are not available in lower resolution ( <; 500-dpi) images. One of the main parameters affecting the quality of a digital fingerprint image and issues such as cost, interoperability, and performance of an AFRS is the choice of image resolution. In this paper, we identify the optimal resolution for an AFRS using the two most representative fingerprint features: minutiae and pores. We first designed a multiresolution fingerprint acquisition device to collect fingerprint images at multiple resolutions and captured fingerprints at various resolutions but at a fixed image size. We then carried out a theoretical analysis to identify the minimum required resolution for fingerprint recognition using minutiae and pores. After experiments on our collected fingerprint images and applying three requirements for the proportions of minutiae and pores that must be retained in a fingerprint image, we recommend a reference resolution of 800 dpi. Subsequent tests have further confirmed the proposed reference resolution.

In recent decades, the classification of fake fingerprints and real fingerprint images has become an attractive research topic because of the most advanced threats. A number of research works have been carried out to classify fake and real fingerprints. But, most of the existing techniques did not utilize swarm intelligence techniques in their fingerprint classification system. Swarm intelligence has been widely used in various applications due to its robustness and potential in solving a complex optimization problem. The proposed classification methodology comprises of four steps, namely image preprocessing, feature extraction, feature selection and classification. This work uses efficient min-max normalization and median filtering for preprocessing, and multiple static features are extracted from Gabor filtering. To perform multiple static feature selection, Artificial Bee Colony (ABC) and Modified Artificial Bee Colony (MABC) optimization algorithm is used in this work to choose the best optimal static features on the basis of specific fitness values. This approach uses Fuzzy Feed Forward Neural Network (FFFNN) for classification and classification of feature vector into fake and real fingerprint images by the semi supervised graph based classification approach which enables efficient classification of real and fake fingerprints and its performance is compared with the SVM classifier. Evaluation of the performance has been carried out by taking the fingerprint images that had been collected from the FVC2000 and synthetically generated database by employing the SFinGE. The results showed that the proposed work provided improvised and enhanced results with respective to characteristics like sensitivity, precision, specification as well as classification accuracy.

Analyze the singularity of the heat flux with a singular boundary element

In this paper, we present an efficient algorithm to compute singular points and singularity-induced bifurcation points of differential-algebraic equations for a multimachine power-system model. Power systems are often modeled as a set of differential-algebraic equations (DAE) whose algebraic part brings singularity issues into dynamic stability assessment of power systems. Roughly speaking, the singular points are points that satisfy the algebraic equations, but at which the vector field is not defined. In terms of power-system dynamics, around singular points, the generator angles (the natural states variables) are not defined as a graph of the load bus variables (the algebraic variables). Thus, the causal requirement of the DAE model breaks down and it cannot predict system behavior. Singular points constitute important organizing elements of power-system DAE models. This paper proposes an iterative method to compute singular points at any given parameter value. With a lemma presented in this paper, we are also able to locate singularity induced bifurcation points upon identifying the singular points. The proposed method is implemented into voltage stability toolbox and simulations results are presented for a 5-bus and IEEE 118-bus systems.