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Table 3 Available latent fingerprinting reconstruction approaches

From: An investigation of latent fingerprinting techniques

Ref. Year Description Database Limitation Results
(Wong & Lai, 2020) 2020 CNN-based fingerprint reconstruction from the corrupted image MOLF, FVC2002 DB1 and FVC2004 DB1 Unsuccessful in extremely low contrast and noisy images Accuracy = 84.10%
(Lee et al., 2020) 2020 Deep Neural Network–based approach for recovery of latent fingerprints NIST Special Database 4   At reconstruction weight = 150
FMR0.01% = 66%
FMR0.1% = 93%
FMR 1% = 100%
(Xu et al., 2020) 2020 Generative adversarial network (GAN) based data augmentation scheme to improve the reconstruction NIST SD14 and MOLF DB 1,2,3 were used at the augmentation stage.   Matching accuracy (%)
NIST SD27: Rank25 = 82.17%
IIITD: Rank25 = 95.12%
Rank25 = 45.88%
(Joshi et al., 2019b) 2019 Reconstruction is done using generative convolutional networks. Gallery datasets like Lumidigm, Secugen, Crossmatch are used False minutiae generation is a challenge Rank 25
Lumidigm = 16.14%
Secugen = 13.27%
Crossmatch = 12.66%
(Dabouei et al., 2018) 2018 ID preserving generative adversarial network is used for partial latent fingerprint reconstruction IIIT-Delhi latent fingerprint database and IIIT-Delhi MOLF database Minutiae are not directly extracted from the latent input fingerprints. Rank 10 accuracy = 88.02% (IIIT-Delhi latent fingerprint database)
rank 50 accuracy = 70.89% IIIT-Delhi MOLF matching
(Li et al., 2018) 2018 Multiscale dictionaries with texture components are used. NIST SD27 Computation for false minutiae removal and repetitive minutiae removal is very high. The average orientation estimation error (in degrees) is 16.38
(Kaushal et al., 2016) 2016 An analytical framework is proposed NIST SD-27 Different filter used for different images False acceptance rate = 27%
(Zhou et al., 2016) 2016 Partial fingerprint indexing–based algorithm is proposed FVC 2000 DB2a, FVC2002 DB1a and NIST SD 14 Indexing is difficult to apply on a very large database Average penetration rate on FVC2002 DB1a when hit rate is 100% = 3.51%
(Cao & Jain, 2015) 2015 ConvNet–based approach for latent orientation field estimation NIST SD27 When latent overlaps with strong background noise, global orientation patch dictionary and ridge structure dictionary approaches do not work well The average root-mean-square deviation (RMSD) is 13.51 as compared with other algorithms.
(, 2019) 2015 Dictionary-based approach FVC2002, NIST SD4, Dictionary lookup is a slow process Improvement in reconstructed image (visual inspection)
(Zhou et al., 2013) 2013 Reconstruction of partial fingerprints Self-created images Tested on few images only that are of good quality Improvement in reconstructed image (visual inspection)
(Wang et al., 2007) 2007 FOMFE-based approach is proposed FVC2002 Db1a database and NIST Special Database 14 (SDB14)   At feature vector length = 15
Penetration rate = 0.21