This document presents a GPU-based algorithm for fingerprint identification in large databases. It discusses biometrics and fingerprint identification terms. It then describes how the algorithm works, including using the Minutia Cylinder-Code (MCC) approach for fingerprint matching and adapting the algorithm for parallel GPU processing. The document outlines the hardware used, including Tesla and GTX GPUs, and presents empirical results showing speed-ups of up to 100.8x compared to CPU and the ability to process over 55,000 fingerprints per second on a single GPU. Future work areas are identified as reducing fingerprint candidates and using multiple fingerprints per person.

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Presented By
Alpesh Kurhade
Under The Guidance Of
Mrs. M. A. Shah
Presented By
Alpesh D. Kurhade
M.Tech I Year
Under the Guidance of
Mrs. M. A. Shah

2. 1. Introduction
2. Related Work
3. Proposed Work
4. System Flow and Methodology
5. Conclusion
6. Future Work
7. References
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3. DEFINITIONOF BIOMETRICS
Biometrics refers to the automatic identification of a person
based on his or her physiological or behavioral characteristics

4. 4
BIOMETRIC TERMS
1.Recognition
2.Identification
3.Verification

5. Fingerprint recognition
 A live acquisition of a person’s
fingerprint.
 Dots (very small ridges),
 Bifurcation,
 It can be characterized through some
particular called minutiae.

7. Biometrics Identification
Biometric
Data
Collection
Transmission
Signal
Processing,
Feature
Extraction,
Representation
Quality
Sufficient?
Generate Template
Template Match
Decision
Confidence?
Yes
No
Database
new biometric sample is requested.
No
Yes

8. 1) Minutiae Matching.
Continue…
 Matching process is the main bottleneck of identification system.
I. Local minutiae matching algorithm: - define neighborhood.
II. Global minutiae matching algorithm: - use the information
of all the minutiae at once.

9. Continue….
2) Minutia Cylinder Code algorithm (MCC)
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This algorithm uses only the position and orientation of
minutiae

10. The similarity is defined by:
The cylinder Ca and Cb are matchable if the directional difference between
two minutiae is not greater than a certain value.

11. It is used in embedded systems, mobile phones, personal
computers, workstations, and game consoles.
Highly parallel structure makes them more effective.
A Single Instruction Multiple Data (SIMD) architecture is
used in GPU devices to introduce parallelism
Some GPU are:
#1 GeForce GTX 580
#2 GeForce GTX 480
#3 Quadro 6000
#4 GeForce GTX 570
#5 GeForce GTX 470
#6 Radeon HD 6870

12. Structure Used By GPU
1. Thread
2. Blocks
3. Grids
4. Wraps

13. A GPU-Based Algorithm
1) The adaptation of the different data structures.
2) Calculation of the algorithm on the GPU.
3) Specific enhancements for identification
systems.

14. 1. Data structures are one of the key issues.
2. Use coalesced memory access
3. Each fingerprint(minutiae) Float4 data type
4. Fingerprint Database constructed using two linear arrays
Data Structures

15. Computation
1. Cylinder Generation:- The number of
cells per cylinder (Ns × Ns × Nd ) is one of the
MCC algorithm parameters.
Fig:graphic scheme of the computational structure

16. 2. Fingerprint Matching.
Continue….
 Comparison between input fingerprint to a set of fingerprints
stored in a database.
 Processes has been adapted to the parallel GPU architecture.

17. Performance Enhancements for
Identification Systems
A fingerprint identification system’s goal is not to perform one
to one fingerprint matches but to find the matching fingerprint
in a database to match an input fingerprint.
A. The reduction of GPU idle periods.
B. The packaging of several matching processes
into one.
Reducing the GPU idling periods

18. Scalability
1. Multi-GPU
2. Fingerprint Database Size

19. A.Experiments
B. Hardware
C.Empirical Results

20. Two different types of GPUs have been used in the experiments:
1) Tesla GPU, an NVIDIA Tesla M2090 with 512 CUDA cores, Fermi
architecture and 6GB of memory.
2) GTX GPU, an NVIDIA GeForce GTX 680 with 1536 CUDA cores, Kepler
Architecture and 2GB of memory.
Hardware

21. Empirical Results

22. Fingerprints per second with the DB4 database.
Continue…

23. a) An efficient GPU based Fingerprint method using MCC algorithm.
b) Obtained speed-up ratios up to 100.8× with respect to a single-thread
CPU implementation.
c) System has no scaling issues when the fingerprint database size
Increases.
d) It is able to perform an identification in a reasonable amount of time for
large databases, processing up to 55,700 fingerprints per second with a
single GPU.
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24. 1. To study other aspects of the fingerprint
identification.
2. The reduction of database fingerprint candidates.
3. Use of several fingerprints from the same person in
the identification process
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25. References
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Pablo David Gutiérrez, Miguel Lastra, Francisco Herrera, and José
Manuel Benítez , ” A High Performance Fingerprint Matching System
for Large Databases Based on GPU,” IEEE Trans. On Information
Forensics And Security , Vol. 9, No.1, Jan 2014.
D. Maltoni, D. Maio, A. Jain, and S. Prabhakar, Handbook of
Fingerprint Recognition. New York, NY, USA: Springer-Verlag, 2009.
R. Cappelli, M. Ferrara, and D. Maltoni, “Minutia cylinder-code: A
new representation and matching technique for fingerprint
recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 12,
pp. 2128–2141, Dec. 2010.
Lin Hong, Student Member, IEEE, Yifei Wan, and Anil Jain, Fellow,
IEEE., ”Fingerprint Image Enhancement :Algorithm and Performance
Evaluation”, IEEE Trans On pattern analysis & machine intellegence,
vol. 20, no. 8, aug.1998.

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