Fast and accurate algorithm for core point detection in fingerprint images

Bahgat, G.A.; Khalil, A. H.; Kader, N.S. Abdel; Mashali, Samia A.

doi:10.1016/j.eij.2013.01.002

Cited by 18 publications

(15 citation statements)

References 17 publications

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“…The result showed that this method was able to handle the fingerprint variety in the dataset. Then, Bahgat et al [27] utilized an orientation map that was presented in grayscale to locate the core point. They assumed that the core point should appear at the end of the discontinuous line in the orientation map.…”

Section: Related Workmentioning

confidence: 99%

Reference point detection for camera-based fingerprint image based on wavelet transformation

Khalil

2015

BioMed Eng OnLine

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BackgroundFingerprint recognition systems essentially require core-point detection prior to fingerprint matching. The core-point is used as a reference point to align the fingerprint with a template database. When processing a larger fingerprint database, it is necessary to consider the core-point during feature extraction. Numerous core-point detection methods are available and have been reported in the literature. However, these methods are generally applied to scanner-based images. Hence, this paper attempts to explore the feasibility of applying a core-point detection method to a fingerprint image obtained using a camera phone.MethodThe proposed method utilizes a discrete wavelet transform to extract the ridge information from a color image. The performance of proposed method is evaluated in terms of accuracy and consistency. These two indicators are calculated automatically by comparing the method’s output with the defined core points.ResultsThe proposed method is tested on two data sets, controlled and uncontrolled environment, collected from 13 different subjects. In the controlled environment, the proposed method achieved a detection rate 82.98%. In uncontrolled environment, the proposed method yield a detection rate of 78.21%.ConclusionThe proposed method yields promising results in a collected-image database. Moreover, the proposed method outperformed compare to existing method.

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Section: Related Workmentioning

confidence: 99%

Reference point detection for camera-based fingerprint image based on wavelet transformation

Khalil

2015

BioMed Eng OnLine

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“…Considering the following facts, a method is proposed to find the bending energy of discrete curvature by modifying the definition mentioned in [31]: (1) Originally, [31] proposed an equation for calculating the average bending energy of any curve, whereas this work concentrates in calculating the bending energy of a single point, (2) it has been assumed that the nearest neighbors also contribute to the calculation of the BE of a specific point, and hence, the bending energy of a point can be approximated as the average bending energy of its neighbors of some length and (3) the BE associated with a horizontal neighbor is more compared to its vertical neighbor because of the distance quantization error associated with the chain code representation.…”

Section: Reference Point Localizationmentioning

confidence: 99%

“…The reference point is taken as the point where the filter response (VSCOME) is maximum. Bahgat et al [2] make use of a mask that locates the core point from the ridge orientation map. Then, the proposed algorithm detects the core point at the end of the discontinuous line appearing in the orientation map.…”

Section: Introductionmentioning

confidence: 99%

Fingerprint reference point identification based on chain encoded discrete curvature and bending energy

Zacharias¹,

Nair

Lal

2016

Pattern Anal Applic

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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.

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“…If the distance is larger than 40 pixels, the reference point is considered a false detected point. In order to estimate the performance of our method, we compared the performance of the proposed reference point detection method with the methods proposed in [19]. The performance comparison is shown in Table 2.…”

Section: Determination Of Reference Point Detectionmentioning

confidence: 99%

An effective algorithm for fingerprint reference point detection based on filed flow curves

Nasiri

2015

2015 the International Symposium on Artificial Intelligence and Signal Processing (AISP)

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In this paper a novel approach is proposed to detect reference point for fingerprint images. Reference point extraction is a key component in automatic fingerprint identification and recognition systems. A new method was proposed for fingerprint reference point extraction, based on field flow curve and clustering. High curvature points in the flow curves are used in our reference point detection. Because we use flow curve instead of ridge for reference point detection, our method is robust to noise and has a good result on fingerprint image with low quality. Also our method has the ability to detect a reference point for an arch class fingerprint which is hard for other methods to detect it. The experiments are conducted on FVC2002-DB2a and FVC2004 to measure the performance of our reference point detection. Experimental results show that our algorithm is robust and it has better results than other approaches. Index Terms-Orientation field, reference point, filed flow curve, curvature, single-link clustering, Poincare index.

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Fast and accurate algorithm for core point detection in fingerprint images

Cited by 18 publications

References 17 publications

Reference point detection for camera-based fingerprint image based on wavelet transformation

Reference point detection for camera-based fingerprint image based on wavelet transformation

Fingerprint reference point identification based on chain encoded discrete curvature and bending energy

An effective algorithm for fingerprint reference point detection based on filed flow curves

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