A New Reference Point Detection Algorithm Based on Orientation Pattern Labeling in Fingerprint Images

Park, Chul-Hyun; Oh, Sung‐Kwun; Kwak, Dong-Min; Kim, Bum Soo; Song, Youngchul; Park, Kil−Houm

doi:10.1007/978-3-540-44871-6_81

Cited by 27 publications

(12 citation statements)

References 4 publications

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“…However, due to the feature detection process is SP-based, our approach can't deal with the fingerprints that have no SPs such as arch type ones. A possible solution may be found in [17] which proposed a method for locating reference points in all types of fingerprints.…”

Section: Resultsmentioning

confidence: 99%

Fingerprint indexing based on singular point correlation

Liu

Zhu

Zhang

et al. 2005

IEEE International Conference on Image Processing 2005

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Fingerprint indexing is an efficient technique that greatly improves the performance of Automated Fingerprint Identification Systems. We propose a continuous fingerprint indexing method based on location, direction estimation and correlation of fingerprint singular points. Location and direction estimation are achieved simultaneously by applying a T-shape model to directional field of fingerprint images. The T-shape model analyzes homocentric sectors around the candidate singular points to find lateral-axes and further main-axes. Then a distortion-tolerant filter of Minimum Average Correlation Energy is utilized to obtain a correlation-based similarity measure which gives the evidence of searching priority. The experiment is performed by 400-fingerprint retrieval from 10,000 templates and the mean search space is only 3.46% of the whole dataset.

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Section: Resultsmentioning

confidence: 99%

Fingerprint indexing based on singular point correlation

Liu

Zhu

Zhang

et al. 2005

IEEE International Conference on Image Processing 2005

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“…The reference point detection algorithm based on the orientation field of fingerprint images has been described in reference [9] and is not detailed here.…”

Section: ) [8]mentioning

confidence: 99%

Fingerprint Recognition System Based on Mapping Approach

Singh¹,

Singh²,

Shukla³

2010

IJCA

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Fingerprint identification system is mainly consisted of fingerprint achieving, fingerprint classification and fingerprint matching. Fingerprint matching is the key to the system and effects on the precision and efficiency of the whole system directly. Fingerprints are matched mainly based on their fingerprint texture pattern which can be described with the orientation field of fingerprints. A fingerprint, which has the different orientation angle structure in different local area of the fingerprint and has a texture pattern correlation among the neighboring local areas of the fingerprint, can be viewed as a Markov stochastic field. A novel method of fingerprint matching, which is based on embedded Hidden Markov Model (HMM) that is used for modeling the fingerprint's orientation field, is described in this paper. The accurate and robust fingerprint matching can be achieved by matching embedded Hidden Markov Model parameters which were built after the processing of extracting features from a fingerprint, forming the samples of observation vectors and training the embedded Hidden Markov Model parameters.

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“…In addition, this method is not effective for reference-point localization in plain arch fingerprint. Park et al [5] proposed an efficient algorithm of referencepoint detection based on orientation pattern labeling. This method is also not consistent to fingerprint rotation and its performance for plain arch fingerprint is inferior to that of the method proposed by Jain et al [4].…”

Section: Introductionmentioning

confidence: 99%

Fingerprint Reference-Point Detection

Liu

Jiang

Kot

2005

EURASIP J. Adv. Signal Process.

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A robust fingerprint recognition algorithm should tolerate the rotation and translation of the fingerprint image. One popular solution is to consistently detect a unique reference point and compute a unique reference orientation for translational and rotational alignment. This paper develops an effective algorithm to locate a reference point and compute the corresponding reference orientation consistently and accurately for all types of fingerprints. To compute the reliable orientation field, an improved orientation smoothing method is proposed based on adaptive neighborhood. It shows better performance in filtering noise while maintaining the orientation localization than the conventional averaging method. The reference-point localization is based on multiscale analysis of the orientation consistency to search the local minimum. The unique reference orientation is computed based on the analysis of the orientation differences between the radial directions from the reference point, which are the directions of the radii emitted from the reference point with equivalent angle interval, and the local ridge orientations along these radii. Experimental results demonstrate that our proposed algorithm can consistently locate a unique reference point and compute the reference orientation with high accuracy for all types of fingerprints.

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A New Reference Point Detection Algorithm Based on Orientation Pattern Labeling in Fingerprint Images

Cited by 27 publications

References 4 publications

Fingerprint indexing based on singular point correlation

Fingerprint indexing based on singular point correlation

Fingerprint Recognition System Based on Mapping Approach

Fingerprint Reference-Point Detection

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