Enhanced Ridge Direction for the Estimation of Fingerprint Orientation Fields

An accurate estimation of fingerprint orientation fields is an important step in the fingerprint classification process. Gradient-based approaches are often used  for estimating orientation fields of ridge structures but this method is susceptible to noise. Enhancement of ridge direction improves the structure of orientation fields and increases the number of correct features thereby conducing the overall performance of the classification process. In this paper, we propose an algorithm to improve orientation field structures using variance of gradient. That algorithm have two steps; firstly, estimation of fingerprint orientation fields using gradient-based method, and finally, enhancement of ridge direction using minimum variance of the cross center block direction. We have used standard fingerprint database NIST-DB14 for testing of proposed algorithm to verify the degree of efficiency of algorithm. The experiment results suggest that our enhanced algorithm achieves visibly better noise resistance with other methods

Fingerprint Enhancement Algorithm Based-on Gradient Magnitude for the Estimation of Orientation Fields

An accurate estimation of fingerprint orientation fields is an important step in the fingerprint classification process. Gradient-based approaches are often used for estimating orientation fields of ridge structures but this method is susceptible to noise. Enhancement of fingerprint images improves the ridge-valley structure and increases the number of correct features thereby conducing the overall performance of the classification process. In this paper, we propose an algorithm to improve ridge orientation textures using gradient magnitude. That algorithm has four steps; firstly, normalization of fingerprint image, secondly, foreground extraction, thirdly, noise areas identification and marking using gradient coherence and finally, enhancement of grey level. We have used standard fingerprint database NIST-DB14 for testing of proposed algorithm to verify the degree of efficiency of algorithm. The experiment results suggest that our enhanced algorithm achieves visibly better noise resistance with other methods

A technique to improve ridge flows of fingerprint orientation fields estimation

An accurate estimated fingerprint orientation fields is a significant step for detection of singular points. Gradient-based methods are frequently used for estimating orientation fields but those methods are sensitive to noise. Fingerprints that perfect quality are seldom. They may be corrupted and degraded due to impression conditions or variations on skin. Enhancement of ridge flows improved the structure of orientation fields and hence increased the number of true singular points thereby conducting the overall performance of the classification process. In this paper, we provided discussion on the technique and implementation to improve local ridge flows of fingerprint orientation fields. That main technique have four steps; firstly, fingerprint segmentation; secondly, identification of noise areas and marking; thirdly, estimation of fingerprint orientation fields, and finally, enhancement of ridge flows using minimum variance of the cross centre block direction in squared gradients. A standard fingerprint database is used for testing of proposed technique to verify the tier of effectivity of algorithm. The experimental results suggest that our enhanced algorithm achieves visibly better ridge flows compare to other methods

Tratamiento digital de huellas dactilares

Este trabajo tiene como objeto el estudio del tratamiento dígital de las huellas digitales. En él se puede ver como los sensores de adquisición de huellas tratan las imágenes y cómo se comparan diferentes huellas para verificar la identidad de un cierto individuo. Por otra parte, se utiliza un criptosistema con propiedades homomórficas para el encriptamiento de las huellas. Su finalidad es tratar de mejorar la seguridad de las bases de datos de los usuarios de ciertos servicios, de manera que la verificación de la identidad de un cierto individuo se pueda seguir realizando

Automatic fingerprint classification scheme using template matching with new set of singular point-based features

Fingerprint classification is a technique used to assign fingerprints into five established classes namely Whorl, Left loop, Right loop, Arch and Tented Arch based on their ridge structures and singular points’ trait. Although some progresses have been made thus far to improve accuracy rates, problem arises from ambiguous fingerprints is far from over, especially in large intra-class and small inter-class variations. Poor quality images including blur, dry, wet, low-contrast, cut, scarred and smudgy, are equally challenging. Thus, this thesis proposes a new classification technique based on template matching using fingerprint salient features as a matching tool. Basically, the methodology covers five main phases: enhancement, segmentation, orientation field estimation, singular point detection and classification. In the first phase, it begins with greyscale normalization, followed by histogram equalization, binarization, skeletonization and ends with image fusion, which eventually produces high quality images with clear ridge flows. Then, at the beginning of the second phase, the image is partitioned into 16x16 pixels blocks - for each block, local threshold is calculated using its mean, variance and coherence. This threshold is then used to extract a foreground. Later, the foreground is enhanced using a newly developed filling-in-the-gap process. As for the third phase, a new mask called Epicycloid filter is applied on the foreground to create true-angle orientation fields. They are then grouped together to form four distinct homogenous regions using a region growing technique. In the fourth phase, the homogenous areas are first converted into character-based regions. Next, a set of rules is applied on them to extract singular points. Lastly, at the classification phase, basing on singular points’ occurrence and location along to a symmetric axis, a new set of fingerprint features is created. Subsequently, a set of five templates in which each one of them represents a specific true class is generated. Finally, classification is performed by calculating a similarity between the query fingerprint image and the template images using x2 distance measure. The performance of the current method is evaluated in terms of accuracy using all 27,000 fingerprint images acquired from The National Institute of Standard and Technology (NIST) Special Database 14, which is de facto dataset for development and testing of fingerprint classification systems. The experimental results are very encouraging with accuracy rate of 93.05% that markedly outpaced the renowned researchers’ latest works

Ridge orientation modeling and feature analysis for fingerprint identification

This thesis systematically derives an innovative approach, called FOMFE, for fingerprint ridge orientation modeling based on 2D Fourier expansions, and explores possible applications of FOMFE to various aspects of a fingerprint identification system. Compared with existing proposals, FOMFE does not require prior knowledge of the landmark singular points (SP) at any stage of the modeling process. This salient feature makes it immune from false SP detections and robust in terms of modeling ridge topology patterns from different typological classes. The thesis provides the motivation of this work, thoroughly reviews the relevant literature, and carefully lays out the theoretical basis of the proposed modeling approach. This is followed by a detailed exposition of how FOMFE can benefit fingerprint feature analysis including ridge orientation estimation, singularity analysis, global feature characterization for a wide variety of fingerprint categories, and partial fingerprint identification. The proposed methods are based on the insightful use of theory from areas such as Fourier analysis of nonlinear dynamic systems, analytical operators from differential calculus in vector fields, and fluid dynamics. The thesis has conducted extensive experimental evaluation of the proposed methods on benchmark data sets, and drawn conclusions about strengths and limitations of these new techniques in comparison with state-of-the-art approaches. FOMFE and the resulting model-based methods can significantly improve the computational efficiency and reliability of fingerprint identification systems, which is important for indexing and matching fingerprints at a large scale