Crease detection from fingerprint images and its applications in elderly people

Zhou, Jie; Chen, Fanglin; Wu, Nannan; Wu, Chenyu

doi:10.1016/j.patcog.2008.09.011

Cited by 23 publications

(17 citation statements)

References 18 publications

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“…Two papers were recently published which especially address the problem of creases and scars: Oliveira and Leite [35] propose to detect and reconnect broken ridges using a multiscale directional operator in combination with morphological tools. In [41] creases are detected for removing spurious minutiae and as a feature for fingerprint matching. B(a, b).…”

Section: B Orientation Field Estimation 1) Evaluation Of Traced Linesmentioning

confidence: 99%

Robust Orientation Field Estimation and Extrapolation Using Semilocal Line Sensors

Gottschlich

Mihăilescu

Munk

2009

IEEE Trans.Inform.Forensic Secur.

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Orientation field (OF) estimation is a crucial preprocessing step in fingerprint image processing. In this paper, we present a novel method for OF estimation that uses traced ridge and valley lines. This approach provides robustness against disturbances caused, e.g. by scars, contamination, moisture or dryness of the finger. It considers pieces of flow information from a larger region and makes good use of fingerprint inherent properties like continuity of ridge flow perpendicular to the flow. The performance of the line-sensor method is compared with the gradients based method and a multiscale directional operator. Its robustness is tested in experiments with simulated scar noise which is drawn on top of good quality fingerprint images from the FVC2000 and FVC2002 databases. Finally, the effectiveness of the line-sensor based approach is demonstrated on 60 naturally poor quality fingerprint images from the FVC2004 database. All orientations marked by a human expert are made available at the journal's and the authors' website for comparative tests.

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Section: B Orientation Field Estimation 1) Evaluation Of Traced Linesmentioning

confidence: 99%

Robust Orientation Field Estimation and Extrapolation Using Semilocal Line Sensors

Gottschlich

Mihăilescu

Munk

2009

IEEE Trans.Inform.Forensic Secur.

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“…The receiver operating curve (ROC) plots false acceptance rate (FAR) versus false rejection rate (FRR) [34], and it is often used to evaluate the performance of recognition systems. FRR is defined as the percentage of genuine matching pairs with matching scores below the threshold value, while FAR is defined as the percentage of imposter matching pairs with scores above the threshold.…”

Section: Methodsmentioning

confidence: 99%

“…Matching methods can be mainly divided into three classes [34]: (1) algorithms that use the image pixel values directly; (2) algorithms that use low-level features, such as edges and corners; and (3) algorithms that use high-level features. The drawback of high-level matching methods is that high-level features first need to be extracted and identified, which is a rather difficult task.…”

Section: Methodsmentioning

confidence: 99%

Confirming the Diversity of the Brain after Normalization: An Approach Based on Identity Authentication

Chen

Liu

et al. 2013

PLoS ONE

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During the development of neuroimaging, numerous analyses were performed to identify population differences, such as studies on age, gender, and diseases. Researchers first normalized the brain image and then identified features that represent key differences between groups. In these studies, the question of whether normalization (a pre-processing step widely used in neuroimaging studies) reduces the diversity of brains was largely ignored. There are a few studies that identify the differences between individuals after normalization. In the current study, we analyzed brain diversity on an individual level, both qualitatively and quantitatively. The main idea was to utilize brain images for identity authentication. First, the brain images were normalized and registered. Then, a pixel-level matching method was developed to compute the identity difference between different images for matching. Finally, by analyzing the performance of the proposed brain recognition strategy, the individual differences in brain images were evaluated. Experimental results on a 150-subject database showed that the proposed approach could achieve a 100% identification ratio, which indicated distinct differences between individuals after normalization. Thus, the results proved that after the normalization stage, brain images retain their main distinguishing information and features. Based on this result, we suggest that diversity (individual differences) should be considered when conducting group analysis, and that this approach may facilitate group pattern classification.

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“…Experiments are conducted on a databases which consists of 1422 fingers (with 8 fingerprints per finger), in which 827 fingers come from the THU database [8], while the other 595 fingers are from the elderly database that used in [13]. All the fingerprints are captured by a sensor from Digital Persona (image size=320 × 512), with 8 fingerprints per finger.…”

Section: Generalizing Gradient Descent Methods and To Get The Thresholmentioning

confidence: 99%

A hierarchical algorithm for multi-feature based fingerprint identification

Chen

Zhou

2010

2010 IEEE International Conference on Image Processing

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There are different features, such as minutiae, orientationbased minutia descriptor, FingerCode, ridge feature map, orientation map, and density map, to represent fingerprints. Previous studies showed that the performance can be improved by combining these features through a fusion strategy. However, the more features are used, the more time is consumed. In fingerprint verification application, it is tolerated for the system is one-to-one matching. But in automatic fingerprint identification systems (AFIS) which is one-to-N matching (N is the number of fingerprint in the database and it is usually very large), it is not acceptable. In this paper, a fast algorithm for multi-feature based fingerprint identification is proposed. A hierarchical strategy is utilized to quickly discard most false matchings in different stages. Experimental results show that the proposed algorithm can reduce the searching time a lot while preserves the searching ratio.

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Crease detection from fingerprint images and its applications in elderly people

Cited by 23 publications

References 18 publications

Robust Orientation Field Estimation and Extrapolation Using Semilocal Line Sensors

Robust Orientation Field Estimation and Extrapolation Using Semilocal Line Sensors

Confirming the Diversity of the Brain after Normalization: An Approach Based on Identity Authentication

A hierarchical algorithm for multi-feature based fingerprint identification

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