Fingerprint indexing based on singular point correlation

Liu, Tong; Zhu, Guocai; Zhang, Chao; Hao, Pengwei

doi:10.1109/icip.2005.1530386

Cited by 16 publications

(2 citation statements)

References 18 publications

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“…Overall, the quality of fingerprints is poor and they suffer from non-linear distortions due to pressure variations on the subject from where the fingerprints are obtained. Researchers [11,12] used ridge-frequency, and ridge-orientation to represent the global ridge patterns. The major limitations of these global features are that they are not good at handling non-linear distortions and are not invariant to geometric transformations.…”

Section: Latent Fingerprint Identification Challenges: Science Vs Fictionmentioning

confidence: 99%

Latent Fingerprint Identification System Based on a Local Combination of Minutiae Feature Points

Deshpande¹,

Malemath

Patil

et al. 2021

SN COMPUT. SCI.

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Latent fingerprints are the fingerprint traces obtained from various surfaces of objects found at the crime scene. Latent fingerprint quality is, therefore, poor and they suffer from non-linear distortions. This is why latent fingerprints contain partial or incomplete minutia information. Developing an automatic fingerprint identification system (AFIS) for latent fingerprints becomes a major challenge because of a small number of minutia features. Moreover, currently developed AFIS for latent fingerprints are not robust against geometric transformations. In this article, based on the existing partial minutiae characteristics, we propose a method for detecting latent fingerprints. To identify an individual with existing nearest neighbor minutia structures, we develop a scale and rotation invariant algorithm called "Ratio of Minutiae Triangles (RMT)". The algorithm utilizes the features which are based on the local minutiae arrangements around a reference minutia. To deal with missing minutiae in latent fingerprints, we consider minutiae clusters based on nearest minutiae neighborhood relation to form hash structures. In the fingerprint retrieval process, these hash structures are used. On the FVC2004 (

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Section: Latent Fingerprint Identification Challenges: Science Vs Fictionmentioning

confidence: 99%

Latent Fingerprint Identification System Based on a Local Combination of Minutiae Feature Points

Deshpande¹,

Malemath

Patil

et al. 2021

SN COMPUT. SCI.

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“…Moreover, De Boer et al [20] combined various fingerprint features and reported retrieval performance on FVC 2000 Db2. Liu et al [21] also reported improved retrieval The objective of this research is to tackle the problem of fingerprint retrieval with the goal to raise recognition rate for the first rank candidate, or to achieve that the nominal candidate is among the few top rank candidates. By improving recognition performance at very low penetration rates of the database, the final stage of fine matching process, if necessary, can be made more efficient and accurate.…”

Section: Introductionmentioning

confidence: 99%

Fingerprint retrieval by spatial modelling and distorted sample generation

Leung

2013

IET Computer Vision

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In this study, the authors extend and refine the process of fingerprint retrieval, with the goal of boosting recognition rates for the first rank candidate and low penetration rates. On top of a baseline retrieval system which extracts Gabor features in multiple directions from fingerprint images, the authors propose spatial modelling techniques to generate artificial samples for training the system. Translational modelling, rotational modelling and distorted sample generation techniques are used to augment the original training set in order to boost the accuracy of fingerprint retrieval. The effectiveness of the models is evaluated using the well‐known National Institute of Standards and Technology database 4. Experimental results, with reference to some leading fingerprint retrieval rates reported in the literature, confirm that the authors’ proposed system is promising in recognition performance.

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