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Table 2 Available latent fingerprint enhancement approaches

From: An investigation of latent fingerprinting techniques

Ref.

Year

Description

Database

Limitation

Results

(Joshi et al., 2021)

2021

Direct de-noise the fingerprints and reconstruct the missing ridge structure without explicitly estimating the orientation field using GAN’s

IIITD-MOLF

IIITD-MSLF

GAN’s are difficult to train as they require a large dataset for accurate results.

NFIQ (lower score means better quality) = 2.64

(Agarwal & Bansal, 2021)

2021

The fusion of pores and minutiae at score level is used to re-rank the minutiae-based latent matcher

IIITD latent fingerprint database

LivDet 2015 database

Less number of minutiae are used. Additional features such as ridge flow map and ridge quality map can improve the performance.

True detection rate RT = 82.89%

Average of the false detection rate RF = 21.2%

(Gupta et al., 2020)

2020

Enhancement and reconstruction of image using the minutiae density and the orientation field direction

Fingerprint verification competition 2002 (FVC2002) and fingerprint verification competition 2004 (FVC2004)

Only local orientation patterns are considered in the proposed method.

Type I attack: TARa = 97.95% on FVC2002 and 94.09% on FVC2004

Type 2 attack: TAR = 49.25% and 50.02% on FVC2002 and FVC2004

(Huang et al., 2020)

2020

A generative adversarial network (GAN) is proposed for the enhancement of latent fingerprint images.

NIST SD27 dataset, NIST SD14

 

Identification rate (%):

Cumulative match characteristics all = 50%

Cumulative match characteristics good = 77%

Cumulative match characteristics bad = 45%

Cumulative match characteristics ugly = 29%

(Horapong et al., 2020)

2020

Two-Stage Spectrum Boosting with Matched Filter and Sparse Autoencoder is used for enhancement

IIT-D MOLF latent fingerprint database

The proposed method depends on high ridge signal strength initially to boost ridge spectra.

Identification rate (%)

Rank 20 = 43%

(Joshi et al., 2021)

2020

A conditional generative adversarial network-based latent fingerprint enhancement algorithm is proposed.

IIITD-MOLF and IIITD-MSLF database

The proposed algorithm generates spurious features when the ridge information is insufficient

NFIQ (lower score means better quality)

= 2.64

(Jhansirani & Vasanth, 2019)

2019

Image enhancement is done using the Gabor function via multiscale patch-based sparse representation

NIST SD27

Dictionary creation and lookup is slow

The best training performance is 7.8717e obtained at epoch 10.

(Joshi et al., 2019a)

2019

Latent fingerprint enhancement algorithm based on generative adversarial networks is used

IIITD-MOLF database and IIITD-MSLFD database

Spurious features are generated when the ridge information is insufficient.

Matching results: Rank-50 accuracy of 35.66% (DB 1)

30.16% (DB 2)

(Manickam & Devarasan, 2019)

2019

An intuitionistic fuzzy set is used for contrast enhancement of fingerprints

Fingerprint verification competition-2004 and IIIT-latent fingerprint database

Imperfect matching in case of presence of background noise and nonlinear ridge distortion

Matching scores

IIIT-latent fingerprint = 0.2702

FVC2004 database 1 = 0.1912

FVC2004 database 2 = 0.2008

(Manickam et al., 2019a)

2019

Scale-Invariant Feature Transformation (SIFT) is used for the enhancement of an image.

FVC2004 and IIIT-latent fingerprint

Does not work well with very poor and partial prints

Linear index of fuzziness

IIIT-latent fingerprint = 0.2702

FVC2004 database 2 = 0.2008

(Liban & Hilles, 2018)

2018

A hybrid model that is a combination of edge directional total variation model (EDTV) and quality image enhancement with lost minutia reconstruction is used.

NIST SD27 database for testing

RMSE, PSNR to measure performance.

Results are not good with ugly images

Overlapping images not considered

RMSE average = 0.018373 (good-quality image)

PSNR average = 82.99068 (good-quality image)

(Chaidee et al., 2018)

2017

The spectral dictionary is used for enhancement

NIST SD27

Failure due to the wide bandwidth of filter which leads to noise leakage into enhancement process

Identification rate

good-quality print = 76%

bad quality = 59%

ugly quality = 35%

(Liu et al., 2014)

2014

Multiscale Patch Based Sparse Representation used for enhancement

NIST SD27

Global ridge structures are ignored

Do not work well for low-quality fingerprints

Identification rate = approx. 64%

(Cao et al., 2014)

2014

Ridge structure dictionary is used for enhancement

NIST SD27 and WVU DB

Confidence measure is poorly defined for the segmentation and enhancement results. Computational efficiency of the algorithm is low

Identification rate

NIST SD27 = 71%

WVU DB = 78%

(Zhang et al., 2013)

2013

Adaptive directional total variation model

NIST SD27

 

Identification accuracy less than 12% (rank20)

(Feng et al., 2012)

2012

Prior knowledge-based approach

NIST SD27

The speed of the proposed algorithm is slow with low-quality latents

Identification rate:

good-quality print = 60%

bad quality = 24%

ugly quality = 11%

(Yoon et al., 2011)

2011

Enhancement using hypothesized orientation fields

NIST SD27

Human markup of minutiae is required.

Performance is poor for bad and ugly-quality latents.

Latent quality assessment is not automatic

Identification rate

good-quality print = 66%

bad quality = 50%

ugly quality = 40%

(Yoon et al., 2010)

2010

Polynomial model and zero pole model

NIST SD27

Uses fixed ridge frequency

Identification accuracy = 35% (rank1)