Latent fingerprint enhancement via robust orientation field estimation

Yoon, Soweon; Feng, Jianjiang; Jain, Anil K.

doi:10.1109/ijcb.2011.6117482

Cited by 66 publications

(50 citation statements)

References 11 publications

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“…In case of latent fingerprints estimation of ridge orientation is challenging, paper [18] has presented a method of enhancing the fingerprint image using ridge orientation in which ridge orientation is estimated using Short Time Fourier Transform. The results indicate that there is a significant improvement in the accuracy of fingerprint recognition systems.…”

Section: Global Methods For Orientation Extractionmentioning

confidence: 99%

“…Ridge orientation feature is used for fingerprints enhancement, segmentation, indexing and matching. Papers [9] [14] [18] [20] [21] have presented fingerprint image enhancement algorithms based on ridge orientation. Ridge patterns can be enhanced through a bank of filters tuned to specific frequencies for different locations in the fingerprints based on ridge orientation.…”

Section: Fig 3: (A) Fingerprint Image (B) Orientation Imagementioning

confidence: 99%

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Fingerprint Ridge Orientation Extraction: A Review of State of the Art Techniques

Biradar¹,

Sarojadevi²

2014

IJCA

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Personal identification based on fingerprints is very popular compared to identification based on other biometric features like iris, gait and face etc. Performance of automatic fingerprints identification systems depends upon the quality of fingerprints. Extraction of fingerprints features in poor quality fingerprints is challenging task. Accurate measurement of fingerprints feature improves the accuracy of identification systems. Fingerprints consists of ridge and valley structures and offers different types of features, they are categorized as level 1, level 2 and level 3 features. Level 1 features are singular points which are used for fingerprints registration, classification etc. Level 2 features are ridge features like minutiae points, ridge orientation etc., and commercially available fingerprint recognition systems are based on level 2 features. Level 3 features include sweat pores, incipient ridges etc. Among these features ridge orientation is used for fingerprint enhancement, fingerprint classification, indexing and fingerprint segmentation. This paper provides an overview of existing state of the art techniques for ridge orientation estimation.

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Section: Global Methods For Orientation Extractionmentioning

confidence: 99%

Section: Fig 3: (A) Fingerprint Image (B) Orientation Imagementioning

confidence: 99%

Fingerprint Ridge Orientation Extraction: A Review of State of the Art Techniques

Biradar¹,

Sarojadevi²

2014

IJCA

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“…Motivated by the combination model in [13] and its successful application for latent fingerprint enhancement [17] and fingerprint reconstruction [18], we first remove the influence of singular points from the orientation field by subtracting θ SP from θ, and then approximate the residual orientation field with a set of basis functions. Let…”

Section: A Orientation Field Modelsmentioning

confidence: 99%

Model Based Separation of Overlapping Latent Fingerprints

Zhao

Jain

2012

IEEE Trans.Inform.Forensic Secur.

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Abstract-Latent fingerprints lifted from crime scenes often contain overlapping prints, which are difficult to separate and match by state-of-the-art fingerprint matchers. A few methods have been proposed to separate overlapping fingerprints to enable fingerprint matchers to successfully match the component fingerprints. These methods are limited by the accuracy of the estimated orientation field, which is not reliable for poor quality overlapping latent fingerprints. In this paper, we improve the robustness of overlapping fingerprints separation, particularly for low quality images. Our algorithm reconstructs the orientation fields of component prints by modeling fingerprint orientation fields. In order to facilitate this, we utilize the orientation cues of component fingerprints, which are manually marked by fingerprint examiners. This additional markup is acceptable in forensics, where the first priority is to improve the latent matching accuracy. The effectiveness of the proposed method has been evaluated not only on simulated overlapping prints, but also on real overlapped latent fingerprint images. Compared with available methods, the proposed algorithm is more effective in separating poor quality overlapping fingerprints and enhancing the matching accuracy of overlapping fingerprints.

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“…Ridge orientation is an important feature because the impact of it is affected to other processes like fingerprint enhancement, classification, segmentation and matching. The ridge orientation can be used in [4][5][6][7][8][9] image enhancement algorithms, singular points detection [5][ [10][11][12][16] and classification [13][14][15][16][17][18][19]. If accurate orientation estimation is not found then false recognition is generate.…”

Section: Introductionmentioning

confidence: 99%

Performance Improvement in Gradient based Algorithm for the Estimation of Fingerprint Orientation Fields

Patel¹,

Parikh²,

Patel³

2017

IJCA

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Accuracy of fingerprint recognition system is reliable for correct measurement of fingerprint features. Orientation estimation of fingerprint ridges is playing a vital role in image enhancement, segmentation, classification and recognition. The accurate estimation of ridge orientation improves the performance of minutiae extraction and matching algorithm. The noisy fingerprint does not contain the clear ridge structure, that's why ridge orientation estimation is the toughest and challenging task in fingerprint image enhancement. Gradientbased orientation estimation algorithm is widely adopted and most popular method accepted in literature. This paper enhance the consistency level of ridge orientation after changing the range of output direction from [-PI/4, PI/4] to [0, PI] and remove the inconsistency. The implementation is done using java language and the experimental result is made on FVC2000 and FingerDOS databases. The outcome of enhanced new method for estimating ridge orientation give better performance than the existing gradient based approach.

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Latent fingerprint enhancement via robust orientation field estimation

Abstract: Abstract

Cited by 66 publications

References 11 publications

Fingerprint Ridge Orientation Extraction: A Review of State of the Art Techniques

Fingerprint Ridge Orientation Extraction: A Review of State of the Art Techniques

Model Based Separation of Overlapping Latent Fingerprints

Performance Improvement in Gradient based Algorithm for the Estimation of Fingerprint Orientation Fields

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