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 | Â | 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 | Â | 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) |