Table 4 Available latent fingerprint matching approaches
Ref. | Year | Description | Database | Limitation | Results |
|---|---|---|---|---|---|
(Deshpande et al., 2020) | 2020 | A clustered minutiae-based scale and rotation invariant fingerprint matching method is proposed | FVC2004 and NIST SD27 criminal fingerprint databases | Matching efficiency is poor in cases where sufficient clustered minutiae set is obtained. | 97.5% and 100% of Rank-1 identification accuracy respectively on plain FVC2004 dataset. Rank-1 identification accuracy of 88.8% and 93.80% respectively on NIST database (LMS, CLMP algorithms) |
(Manickam et al., 2019b) | 2019 | Matching using SIFT feature | FVC2004 and IIIT-latent fingerprint | Database size is small. The feature set used is small. | Linear index of fuzziness IIIT-latent fingerprint = 0.2702 FVC2004 database 1 = 0.1912 FVC2004 database 2 = 0.2008 |
(Ezeobiejesi & Bhanu, 2018) | 2018 | Matching is patch-based using a deep learning approach. | NIST SD27 | The approach does not work well with mixed image resolutions | Rank-20 identification rate = 93.65% |
(Lin & Kumar, 2018) | 2018 | Minutia Spherical Coordinate Code is used for matching | AFIS data and NIST special data27 | There are many redundancies in MCC and MSCC’s feature | Rank-1 recognition rate = 49.2% |
(Ezhilmaran & Adhiyaman, 2017) | 2017 | Descriptor-based Hough transform used for matching | (NIST SD27 and WVU latent databases | Latent matching is slow | Rank-1 accuracy = 53.5% |
(Zhou et al., 2017) | 2017 | The fusion of various extended features to improve performance | NIST SD4, SD14, and SD27 databases | The separation of feature extraction and matching in automatic systems leads to some information loss. | Rank-1 identification rate of 74% was achieved |
(Medina-Pérez et al., 2016) | 2016 | Local and global matching | NIST SD27(A) | Approaches used for level 2 and level 3 matching are different which decreases accuracy | Rank-1 identification accuracy of 74% |
(Zheng et al., 2015) | 2015 | CovNet and Dictionary-based approach | NIST SD27 and WVU latent databases | Recognition performance can be improved. Speed of feature extraction and comparison can be raised | Superior performance of texture (virtual minutiae) template on bad and ugly images (i.e. 47.1%; good-quality image is 83%) |
(Cao et al., 2014) | 2014 | Extended features used for performance enhancement | NIST SD27 | Differences in the approach used by latent experts and automatic matches. Prone to false minutiae and distortions. Information loss due to separation of automatic matching and feature extraction. | Identification rate Good images = 90% Bad images = 85% Ugly images = 71% |
(Lan et al., 2019). | 2014 | A new feature Distinctive Ridge Point (DRP) is proposed | NIST14 and NIST4 | High ridge point dependence with minutiae. | Rank-1 accuracy = 70.9% |
(Jain & Feng, 2010b) | 2014 | Algorithm based on directional information | FVC2004 DB1, Tsinghua Distorted Fingerprint database, NIST SD27 database and NIST SD30 database. | Do not consider the rotation and translation of the whole image | Identification rate using Correlation score = 80% Verifinger score = 82% |
(Feng, 2012) | 2012 | Descriptor-based Hough transform algorithm | NIST SD27 and WVU latent database | Do not work well with overlapping fingerprints | Identification rate = 67% |
(https://www.nist.gov/itl/iad/image-group/nist-special-database-2727a, 2019) | 2012 | Two minutiae-based descriptors are proposed | FingerPass and Multi-Sensor Optical and Latent Fingerprint | For different sensor technology, performance is not good Poor performance when fingerprints were distorted | False matching rate = 1.166% Equal error rate = 0.41% |
(Jain & Feng, 2010b). | 2010 | Fusion of minutiae | NIST SD27 | Orientation field reconstruction to be improved | Identification rate = 65% (manually marked minutiae) |
(Jain & Cao, 2015) | 2009 | Fusion of plain and rolled fingerprints | ELFT-EFS Public Challenge Dataset | Does not appear to be a common practice in law enforcement | Rank-1 identification rate of 83.0% |
(Feng et al., 2009) | 2009 | Fusion of plain and rolled fingerprints | NIST SD27 | The distortion between rolled and plain fingerprints is not taken into account. Manual extraction of level 1 and level 2 features | Rank-1 identification rate = 83.0% |
(Feng & Jain, 2008) | 2008 | Filtering-based approach | NIST SD27 | Singular point detection is not accurate More filtering approaches can be used to improve performance. Background database is small | Rank-1 matching accuracy = 73.3% |