Authentication using neural network for detecting first level pattern of fingerprint and second level pattern of fingerprint.

1. Fingerprint Recognition in
Smart Phones
Presenter: Mohamad Reza Jebeli Haji Abadi
Supervisor: Dr. Safabakhsh

2. o History
o Biometrics characteristics
o Fingerprint
o Authentication
o Conclusion
o References
Content

3. o First phone with fingerprint sensor
• 2004
• Pantech G100
History
o Revolution
• 2013
• iPhone 5s
History Introduction Authentication Conclusion References 1

4. Biometrics
Characteristics
History Introduction Authentication Conclusion References
o Physiological
• Fingerprint
• Face
• Iris
• ...
o Behavioral
• voice
• sign
• ...
2

5. Fingerprint
o Advantages
• Low cost
• High distinctiveness
• High permanence
• High performance
o Ridge
o Valley
History Introduction Authentication Conclusion References 3

6. First level
patterns
Tented Arch Arch Left Loop Right Loop Whorl
Arch Loop
5% 65%
30%
History Introduction Authentication Conclusion References 4

7. Second level
patterns
Termination Bifurcation Short ridge Dot Enclosure Break Crossbar
Overlap Bridge
Opposite
bifurcations Trifurcation Dock Return Spike
History Introduction Authentication Conclusion References 5

8. o Capturing
o Enhancement
o Neural Network
o Detection of first level pattern
o Detection of second level pattern
o Matching
Authentication
History Introduction Authentication Conclusion References 6

9. o Optical Sensors
• Reflection of light from a prism
• Cleanliness
• Low security
o Capacitive Sensors
• voltage
• High security
Capturing
History Introduction Authentication Conclusion References 7

10. Enhancement
o Increasing clarity
o Using filters
• Gabor Filter
History Introduction Authentication Conclusion References 8

11. Neural
Network
o Inputs
o Outputs
o Training with train data
History Introduction Authentication Conclusion References 9

12. Detecting first
level pattern
Neural Network
outputsInputs
..
..
..
o Inputs : fingerprint
o Outputs : first level patterns
History Introduction Authentication Conclusion References 10

13. Detecting second
level pattern
Neural Network
OutputsInputs
..
..
..
o Inputs : fingerprint
o Outputs :
..
..
..
Number of each
second level pattern
and their relative
position from each
other
History Introduction Authentication Conclusion References 11

14. Matching
o Comparison with saved fingerprints which have same first level
pattern
o Algorithm BOZORTH3
Level 2
patterns
vector
Level 2
patterns
vector
Matching? Authentication
History Introduction Authentication Conclusion References 12

15. Conclusion
o History
o Fingerprint introduction
o Authentication
Capturing
Enhancement
First neural
networkTrain
Data
Second
neural
network
Feature
vector
Set of same first
level pattern
fingerprints
Feature
vector
BOZORTH3 Authentication
History Introduction Authentication Conclusion References 13

16. References
[1] Pavol Mark, Alexander Hambalik, “FINGERPRINT RECOGNITION
SYSTEM USING ARTIFICIAL NEURAL NETWORK AS FEATURE
EXTRACTOR: DESIGN AND PERFORMANCE EVALUATION ” (2016)
[2] Sainath Maddala, Sreekanth Rao Tangellapally , “Implementation and
Evaluation of NIST Biometric Image Software for Fingerprint Recognition”
(2010)
[3] Davide Maltoni, “A Tutorial on Fingerprint Recognition ” (2016)
[4] https://www.bayometric.com/minutiae-based-extraction-fingerprint-recognition
History Introduction Authentication Conclusion References 14

18. Thank you for
your attention