The most reliable biometrics technology, fingerprint recognition is widely used in terms of security due to its permanence and uniqueness. However, it is also vulnerable to the certain type of attacks including presenting fake fingerprints to the sensor which requires the development of new and efficient protection measures. Particularly, the aim is to identify the most recent literature related to the fake fingerprint recognition and only focus on software-based approaches. A systematic review is performed by analyzing 146 primary studies from the gross collection of 34 research papers to determine the taxonomy, approaches, online public databases, and limitations of the fake fingerprint. Fourteen software-based approaches have been briefly described, four limitations of fake fingerprint image were revealed and two known fake fingerprint databases were addressed briefly in this review. Therefore this work provides an overview of an insight into the current understanding of fake fingerprint recognition besides identifying future research possibilities.
REFERENCES
1.
Z.
Xia
, R.
Lv
, Y.
Zhu
, P.
Ji
, H.
Sun
, and Y. Q.
Shi
, “Fingerprint liveness detection using gradient-based texture features
,” Signal, Image Video Process.
, vol. 11
, pp. 1
–8
, 2016
.2.
A.
Al-Ajlan
, “Survey on fingerprint liveness detection
,” 2013 Int. Work. Biometrics Forensics
, 2013
, pp. 1
–5
, 2013
.3.
A.
Toosi
, S.
Cumani
, and A.
Bottino
, “On Multiview Analysis for Fingerprint Liveness Detection
,” in Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: 20th Iberoamerican Congress, CIARP 2015
, Montevideo, Uruguay
, November 9-12, 2015
, Proceedings, A.
Pardo
and J.
Kittler
, Eds. Cham
: Springer International Publishing
, 2015
, pp. 143
–150
.4.
E.
Marasco
and A.
Ross
, “A Survey on Antispoofing Schemes for Fingerprint Recognition Systems
,” ACM Comput. Surv.
, vol. 47
, no. 2
, pp. 1
–36
, 2014
.5.
U.
Uludag
and A. K.
Jain
, “Attacks on biometric systems: a case study in fingerprints
,” Proc. SPIE 5306, Secur. Steganography, Watermarking Multimed. Contents
, p. 622
, 2004
.6.
N. K.
Ratha
, J. H.
Connell
, and R. M.
Bolle
, “An Analysis of Minutiae Matching Strength
,” Audio-Video-Based Biometric Pers. Authentication
, vol. 2091
, pp. 223
–228
, 2001
.7.
A.
Hadid
, N.
Evans
, S.
Marcel
, and J.
Fierrez
, “Biometrics Systems Under Spoofing Attack: An evaluation methodology and lessons learned
,” IEEE Signal Process. Mag.
, vol. 32
, no. 5
, pp. 20
–30
, 2015
.8.
E.
Park
, W.
Kim
, Q.
Li
, H.
Kim
, and J.
Kim
, “Fingerprint liveness detection using CNN features of random sample patches: Liveness detection using CNN features
,” Lect. Notes Informatics (LNI), Proc. - Ser. Gesellschaft fur Inform.
, vol. P-260
, 2016
.9.
G.
Arunalatha
and M.
Ezhilarasan
, “Fingerprint Spoof Detection Using Quality Features
,” Int. J. Secur. Its Appl.
, vol. 9
, no. 10
, pp. 83
–94
, 2015
.10.
V.
Mura
, F. R. L.
Ghiani
, G.L.
Marcialis
, D. A.
Yambay
, and S. A. S.
Clarkson
, “Livdet 2015 fingerprint liveness detection competition 2015
,” Int. Conf. Biometrics 2013
, 2013
.11.
L.
Ghiani
, V.
Mura
, S.
Tocco
, G. L.
Marcialis
, F.
Roli
, D.
Yambay
, and S.
Schuckers
, “LivDet 2013 - Iris Liveness Detection Competition 2013
,” Biometrics Theory Appl. Syst.
, 2013
.12.
P.
Achimugu
, A.
Selamat
, R.
Ibrahim
, and M.
Naz
, “A systematic literature review of software requirements prioritization research
,” Inf. Softw. Technol.
, vol. 56
, no. 6
, pp. 568
–585
, 2014
.13.
E.
Fielt
, W.
Bandara
, S.
Miskon
, and G.
Gable
, “Exploring shared services from an is perspective: A literature review and research agenda
,” Commun. Assoc. Inf. Syst.
, vol. 34
, no. 1
, pp. 1001
–1040
, 2014
.14.
J.
Galbally
, F.
Alonso-Fernandez
, J.
Fierrez
, and J.
Ortega-Garcia
, “A high performance fingerprint liveness detection method based on quality related features
,” Futur. Gener. Comput. Syst.
, vol. 28
, no. 1
, pp. 311
–321
, 2012
.15.
P.
Lapsley
, J. L.
Alexander
, D.
Pare
, and N.
Hoffman
, “Anti-fraud biometric scanner that accurately detects blood flow
,” 1998
.16.
A.
Antonelli
, R.
Cappelli
, D.
Maio
, and D.
Maltoni
, “Fake finger detection by skin distortion analysis
,” IEEE Trans. Inf. Forensics Secur.
, vol. 1
, no. 3
, pp. 360
–373
, 2006
.17.
D.
Baldisserra
, A.
Franco
, D.
Maio
, and D.
Maltoni
, “Fake Fingerprint Detection by Odor Analysis
,” Adv. Biometrics
, pp. 265
–272
, 2006
.18.
S.
Kim
, B.
Park
, B. S.
Song
, and S.
Yang
, “Deep belief network based statistical feature learning for fingerprint liveness detection
,” Pattern Recognit. Lett.
, vol. 77
, pp. 58
–65
, 2016
.19.
Z.
Akhtar
, C.
Micheloni
, and G. L.
Foresti
, “Biometric Liveness Detection: Challenges and Research Opportunities
,” IEEE Secur. Priv.
, vol. 13
, no. 5
, pp. 63
–72
, 2015
.20.
R. F.
Nogueira
, R. de Alencar
Lotufo
, and R. C.
Machado
, “Fingerprint Liveness Detection using Convolutional Networks
,” Ieee Trans. Inf. Forensics Secur.
, vol. 11
, no. 6
, pp. 1206
–1213
, 2016
.21.
C.
Wang
, K.
Li
, Z.
Wu
, and Q.
Zhao
, “A DCNN Based Fingerprint Liveness Detection Algorithm with Voting Strategy
,” Springer Int. Publ. Switz.
2015
, pp. 241
–249
, 2015
.22.
J.
Galbally
, S.
Marcel
, and J.
Fierrez
, “Image Quality Assessment for Fake Biometric Detection : Application to Iris, Fingerprint, and Face Recognition
,” IEEE Trans. Image Process.
, vol. 23
, no. 2
, pp. 710
–724
, 2014
.23.
A.
Bhanarkar
, P.
Doshi
, A.
Abhyankar
, and A.
Bang
, “Joint time frequency analysis based liveness fingerprint detection
,” 2013 IEEE 2nd Int. Conf. Image Inf. Process. IEEE ICIIP
2013
, pp. 166
–169
, 2013
.24.
D.
Gragnaniello
, G.
Poggi
, C.
Sansone
, and L.
Verdoliva
, “Local contrast phase descriptor for fingerprint liveness detection
,” Pattern Recognit.
, vol. 48
, no. 4
, pp. 1046
–1054
, 2015
.25.
D.
Gragnaniello
, G.
Poggi
, C.
Sansone
, and L.
Verdoliva
, “Fingerprint liveness detection based on Weber Local image Descriptor
,” 2013 IEEE Work. Biometric Meas. Syst. Secur. Med. Appl. BioMS 2013 - Proc.
, 2013
.26.
R.
Dubey
, J.
Goh
, and V.
Thing
, “Fingerprint Liveness Detection From Single Image Using Low Level Features and Shape Analysis
,” IEEE Trans. Inf. Forensics Secur.
, vol. 6013
, no. c
, pp. 1
–1
, 2016
.27.
Z.
Akhtar
, C.
Micheloni
, and G. L.
Foresti
, “Correlation Based Fingerprint Liveness Detection
,” IEEE
, pp. 305
–310
, 2015
.28.
X.
Jia
, X.
Yang
, K.
Cao
, Y.
Zang
, N.
Zhang
, R.
Dai
, X.
Zhu
, and J.
Tian
, “Multi-scale local binary pattern with filters for spoof fingerprint detection
,” Inf. Sci. (Ny).
, vol. 268
, pp. 91
–102
, 2014
.29.
S.
Mohammadi
and M.
Hariri
, “New Approaches to Fingerprint Authentication Using Software Methods Based on Fingerprint Texture
,” in 2015 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI)
, 2015
, pp. 1088
–1092
.30.
B.
Tan
and S.
Schuckers
, “New approach for liveness detection in fingerprint scanners based on valley noise analysis
,” J. Electron. Imaging
, vol. 17
, no. 1
, p. 11009
, 2008
.31.
Y. S.
Moon
, J. S.
Chen
, K. C.
Chan
, K.
So
, and K. C.
Woo
, “Wavelet based fingerprint liveness detection
,” Trans. Korean Inst. Electr. Eng.
, vol. 57
, no. 6
, pp. 982
–984
, 2008
.32.
R.
Derakhshani
, S. A. C.
Schuckers
, L. A.
Hornak
, and L.
O’Gorman
, “Determination of vitality from a non-invasive biomedical measurement for use in fingerprint scanners
,” Pattern Recognit.
, vol. 36
, no. 2
, pp. 383
–396
, 2003
.33.
A.
Abhyankar
and S.
Schuckers
, “Integrating a wavelet based perspiration liveness check with fingerprint recognition
,” Pattern Recognit.
, vol. 42
, no. 3
, pp. 452
–464
, 2009
.34.
“
LivDet - Liveness Detection Competitions
.” [Online]. Available: http://livdet.org/. [Accessed: 08-Feb-2017].35.
“
ATVS - Biometric Recognition Group » Databases » ATVS-FFp
.” [Online]. Available: http://atvs.ii.uam.es/ffp_db.html. [Accessed: 08-Feb-2017].
This content is only available via PDF.
© 2017 Author(s).
2017
Author(s)
