Fingerprint is one of the most famous biometric characteristics. It is employed in many fields such as forensic, security, recognition and classification. This paper focuses on clustering fingerprint images into original and fake. Unsupervised Deep Leaning (UDL) is proposed, it exploits the Self-Organization Maps (SOM) to provide such clustering. It consists of two internal processing parts. The first part is for the feature extraction. The second part is for the unsupervised clustering of the SOM. Fingerprint images from the ATVS-FakeFingerprint DataBase (ATVS-FFpDB) for without cooperation are utilized in our work. Multiple clustering and classification metrics of the Silhouette Value (SV), Calinski Harabasz Index (CHI), Davies-Bouldin Index (DBI) and accuracy are provided. Also, different comparisons with state-of-the-art Deep Learning (DL) architectures are provided. Our UDL approach has achieved a high accuracy result of 92.86% and fingerprint images are successfully clustered into original and fake categories.

1.
M. Hamed
Izadi
, and
Andrzej
Drygajlo
, “
Estimation of cylinder quality measures from quality maps for Minutia-Cylinder Code based latent fingerprint matching
”,
EU MC ITN BBfor2 — COST IC 1106
,
2013
. ISSN 2351-9738.
2.
Ifeoma U.
Ohaeri
,
Michael
Esiefarienrhe
, and
Naison
Gasela
, “
Multimodal Biometrics as Attacks Measure in Biometrics Systems
”, In
Proceedings of the International Conference on Wireless Networks (ICWN)
, p.
189
,
The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp)
,
2015
.
3.
Abdulsattar M.
Ibrahim
,
Abdulrahman K.
Eesee
, and
Raid Rafi Omar
Al-Nima
, “
Deep Fingerprint Classification Network
”,
TELKOMNIKA Telecommunication, Computing, Electronics and Control
, Vol.
19
, No.
3
,
2021
.
4.
Davide
Maltoni
,
Dario
Maio
,
Anil K.
Jain
, and
Salil
Prabhakar
, “
Handbook of fingerprint recognition
”,
Springer Science & Business Media
,
2009
.
5.
Ram P.
Krish
,
Julian
Fierrez
,
Daniel
Ramos
,
Fernando
Alonso-Fernandez
, and
Josef
Bigun
, “
Improving automated latent fingerprint identification using extended minutia types
”,
Information Fusion
, Vol.
50
,
2019
.
6.
Emanuela
Marasco
and
Arun
Ross
, “
A survey on antispoofing schemes for fingerprint recognition systems
”,
ACM Computing Surveys (CSUR)
, Vol.
47
, No.
2
,
2014
.
7.
David
Zhang
,
Feng
Liu
,
Qijun
Zhao
,
Guangming
Lu
, and
Nan
Luo
, “
Selecting a reference high resolution for fingerprint recognition using minutiae and pores
”,
IEEE Transactions on Instrumentation and Measurement
, Vol.
60
, No.
3
,
2011
.
8.
Crystal
Huynh
and
Jan
Halámek
, “
Trends in fingerprint analysis
”,
TrAC Trends in Analytical Chemistry
, Vol.
82
,
2016
.
9.
Joshua J.
Engelsma
,
Kai
Cao
, and
Anil K.
Jain
, “
Raspireader: Open source fingerprint reader
”,
IEEE transactions on pattern analysis and machine intelligence
, Vol.
41
, No.
10
,
2018
.
10.
Ziad
Alqadi
,
Mohammad
Abuzalata
,
Yousf
Eltous
, and
Ghazi M.
Qaryouti
, “
Analysis of fingerprint minutiae to form fingerprint identifier
”,
JOIV: International Journal on Informatics Visualization
, Vol.
4
, No.
1
,
2020
.
11.
Jannis
Priesnitz
,
Christian
Rathgeb
,
Nicolas
Buchmann
,
Christoph
Busch
, and
Marian
Margraf
, “
An overview of touchless 2D fingerprint recognition
”,
EURASIP Journal on Image and Video Processing
2021
, No.
1
,
2021
.
12.
Sarah Othman
Ali
,
Raid Rafi Omar
Al-Nima
, and
Emad Ahmed
Mohammed
, “
Communication Establishment Based on Authenticating Earprints
”,
International Journal of Future Generation Communication and Networking
, Vol.
14
, No.
1
, pp.
3242
3264
,
2021
.
13.
Marwa Mawfaq
Mohamedsheet
, “
CLASSIFYING THE BRAIN ACTIVITIES OF VISION, MOVEMENT AND PRE-FRONTAL BASED ON MULTIPLE VIEWS AND ARTIFICIAL TECHNIQUES
”, Master Thesis,
Department of Information Engineering, Università Politecnica delle Marche (UNIVPM
),
Ancona, Italy
,
2020
.
14.
L.V.
Fausett
, “
Fundamentals of Neural Networks: Architectures, Algorithms, and Applications
”,
Prentice-Hall Int. Snc
.,
1994
.
15.
P. J.
Rouseeuw
, “
Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
”,
Journal of Computational and Applied Mathematics
, Vol.
20
, No.
1
,
1987
, pp.
53
65
.
16.
Calinski
,
T.
, and
J.
Harabasz
, “
A dendrite method for cluster analysis
”,
Communications in Statistics
, Vol.
3
, No.
1
,
1974
, pp.
1
27
.
17.
MATLAB
, “
Cluster Visualization and Evaluation
”,
The MathWorks Inc
.,
2020
.
18.
Davies
,
D. L.
, and
D. W.
Bouldin
. “
A Cluster Separation Measure
.”
IEEE Transactions on Pattern Analysis and Machine Intelligence.
Vol.
PAMI-1
, No.
2
,
1979
, pp.
224
227
.
19.
ATVS-FakeFingerprint DATABASE (ATVS-FFp DB)
”. [Online]. Available: http://atvs.ii.uam.es/atvs/ffp_db.html.
20.
J.
Galbally
,
F.
Alonso-Fernandez
,
J.
Fierrez
and
J.
Ortega-Garcia
, “
A High Performance Fingerprint Liveness Detection Method Based on Quality Related Features
,”
Future Generation Computer Systems
, vol.
28
, no.
1
, pp.
311
321
,
2012
.
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