Directional Filter Bank-Based Fingerprint Feature Extraction and Matching

Park, Chul-Hyun; Lee, Joon-Jae; Smith, M.J.T.; Park, Sang-Il; Park, Kil−Houm

doi:10.1109/tcsvt.2003.818355

Cited by 59 publications

(26 citation statements)

References 18 publications

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“…This approach is also called continuous classification [10], [11], [12]. Field orientation that is commonly used in building the vectors of numerical features containing local orientations [13], [14], [15], [16]. Besides that, the average range of the fingerprint is also used as an assistance feature in certain researches [11], [17].…”

Section: Proposed Methodsmentioning

confidence: 99%

Analysis of Galton-Henry Classification Method for Fingerprint Database FVC 2002 and 2004

Mukhaiyar¹

2017

GEOMATE

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According to Galton-Henry, fingerprint classification varies into arch, tented arch, left loop, right loop, whorl, and twin loop schemes. The approach is depended on the existence of a core and delta of each fingerprint. If this method is implemented for all fingerprint benchmark databases, then less than 41% of the fingerprint is being classified, by 37.19% for FVC 2002 and 40.31% for FVC 2004. Therefore, in this research, three requirements are needed to improve the classification result of the fingerprint, i.e. core point and its number, ridge frequency and ridge direction, and tented arch as additional requirement. This approach improves the result which is only 5.94% and 1.56% that is unclassified for FVC 2002 and 2004 consecutively. Then, to evaluate the time taken in executing the algorithm, this research does the evaluation by offering two possible conditions of the input of the fingerprint. The first type is without the fingerprint classification, while the second type is with the classification step along the algorithm process. The latter type requires an additional step namely RoI selection process to select a desired area of the fingerprint.

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Section: Proposed Methodsmentioning

confidence: 99%

Analysis of Galton-Henry Classification Method for Fingerprint Database FVC 2002 and 2004

Mukhaiyar¹

2017

GEOMATE

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“…For example, in [2], it proposes a new image-based fingerprint matching method for various rotations and translation of fingerprint input. This approach combines direction of ridges as a prominent feature component and describes fingerprint as directional energies.…”

Section: Related Workmentioning

confidence: 99%

Core-Point, Ridge-Frequency, and Ridge-Orientation Density Roles in Selecting Region of Interest of Fingerprint

Mukhaiyar¹

2017

GEOMATE

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ABSTRACT:In fingerprint recognition technology, ridge-frequency and -orientation step are utilized to identify trait pattern of ridge/valley features of the fingerprint by separating foreground and background of the fingerprint image and specifying the directional pattern of the ridges and valleys. The ability to determine the patterns help us as well to select a particular area to be utilized forth. Meanwhile, core-point is needed as a reference point in region of interest (RoI) cropping process. For example, if a desired region is a squareform area, core-point can be as a central of the RoI. Thus, we just need to lay down two diagonal side of the selecting area. In conclusion, it is obvious that the superiority of these three steps can be exploited in RoI step.

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“…Let Skθ denotes the sub-band image at k level and θ direction. Similarly, let σθk denotes the standard deviation of the kth block in the θ direction sub-band image and cθk (x, y) is the contourlet coefficient value at pixel (x, y) in the sub-band block Skθ , then the value for directional energy Ekθ for that sub-band block is calculated using following equation [43]:…”

Section: Proposed Algorithm For Fingerprintmentioning

confidence: 99%

User Psychology and Behavioral Effect on Palmprint and Fingerprint Based Multimodal Biometric System

Mumtaz¹,

Masood²,

Mumtaz³

2012

AJSP

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The ever increasing demand of security has resulted in wide use of Biometric systems. Despite overcoming the traditional verification problems, the unimodal systems suffer from various challenges like intra class variation, noise in the sensor data etc, affecting the system performance. These problems are effectively handled by multimodal systems. User psychology is accounted has been taken into consideration which highly effects its eventual user acceptability and collectability among masses. Desirable image quality is achieved by a stable and user friendly image acquisition platform. In this paper, we present multimodal approach for palm and fingerprints by feature level and score level fusions (sum and product rules). The proposed multimodal systems are tested on a developed database consisting of 440 palm and fingerprints each of 55 individuals. In feature level fusion, directional energy based feature vectors of palm-and fingerprint identifiers are combined to form joint feature vector that is subsequently used to identify the individual using a distance classifier. In score level fusion, the matching scores of individual classifiers are fused by sum and product rules. Receiver operating characteristics curves are formed for unimodal and multimodal systems. Equal Error Rate (EER) of 0.538% for feature level fusion shows best performance compared to score level fusion of 0.6141 and 0.5482% of sum and product rules, respectively. Multimodal systems, however, significantly outperform unimodal palm and fingerprints identifiers with EER of 2.822 and 2.553%, respectively.

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Directional Filter Bank-Based Fingerprint Feature Extraction and Matching

Cited by 59 publications

References 18 publications

Analysis of Galton-Henry Classification Method for Fingerprint Database FVC 2002 and 2004

Analysis of Galton-Henry Classification Method for Fingerprint Database FVC 2002 and 2004

Core-Point, Ridge-Frequency, and Ridge-Orientation Density Roles in Selecting Region of Interest of Fingerprint

User Psychology and Behavioral Effect on Palmprint and Fingerprint Based Multimodal Biometric System

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