handwritten signatures authentication using anns committee machines m.heinen, f. osório and p....
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October 2007
1
Handwritten Signatures Authentication using ANNs Committee MachinesM.Heinen, F. Osório and P. Engel
Handwritten Signatures Authentication Using Artificial Neural Networks
Committee Machines
Milton Roberto HeinenMilton Roberto Heinen - Applied Computing / Unisinos and II / UFRGS- Applied Computing / Unisinos and II / UFRGS
Prof. Dr. Prof. Dr. Fernando Fernando S. Osório -S. Osório - Applied Computing / UnisinosApplied Computing / Unisinos
Prof. Dr. Paulo M. Engel - Informatics Institute / UFRGSProf. Dr. Paulo M. Engel - Informatics Institute / UFRGS
Applied Computing - PIPCA / UnisinosApplied Computing - PIPCA / UnisinosInformatics Institute - II / UFRGSInformatics Institute - II / UFRGS
UNISINOS University and UFRGS University - Brazil UNISINOS University and UFRGS University - Brazil Web: http://inf.unisinos.br/~osorio/Web: http://inf.unisinos.br/~osorio/NeuralSignXNeuralSignX
CLEI 2007 San José - Costa Rica - October 2007
October 2007
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Handwritten Signatures Authentication using ANNs Committee MachinesM.Heinen, F. Osório and P. Engel
Handwritten Signatures Authentication Using Artificial Neural Networks
Committee Machines
Milton Roberto HeinenMilton Roberto Heinen - Applied Computing / Unisinos and II / UFRGS- Applied Computing / Unisinos and II / UFRGS
Prof. Dr. Prof. Dr. Fernando Fernando S. Osório -S. Osório - Applied Computing / UnisinosApplied Computing / Unisinos
Prof. Dr. Paulo M. Engel - Informatics Institute / UFRGSProf. Dr. Paulo M. Engel - Informatics Institute / UFRGS
Applied Computing - PIPCA / UnisinosApplied Computing - PIPCA / UnisinosInformatics Institute - II / UFRGSInformatics Institute - II / UFRGS
UNISINOS University and UFRGS University - Brazil UNISINOS University and UFRGS University - Brazil Web: http://inf.unisinos.br/~osorio/Web: http://inf.unisinos.br/~osorio/NeuralSignXNeuralSignX
CLEI 2007 San José - Costa Rica - October 2007
Presented by Cássia Nino - Applied Computing / Unisinos
October 2007
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Handwritten Signatures Authentication using ANNs Committee MachinesM.Heinen, F. Osório and P. Engel
Presentation Topics
Agenda:
1. Motivation and Context
2. Handwritten Signature Authentication
3. NeuralSignX System
a) Modules: Acquisition, Pre-Processing, ANN Classification
b) Specialists Committee
4. Description of Experiments
5. Signature Authentication Results
6. Conclusions
October 2007
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Handwritten Signatures Authentication using ANNs Committee MachinesM.Heinen, F. Osório and P. Engel
1. Motivation and Context
Motivation: Security
How to guarantee a legitimate access to resources:Data access, Bank access, Passport, Credit Card, etc.
Do you really are who you claim to be ??
Context: Biometric Systems Signature
Identify: who you are Off-LineVerify: authenticate one user Scanned Signature (static)
Physical features: On-Line Fingerprints, hand, eyes, ... Digital Pen (dynamic)Behavioral features: Signature, keyboard typing, voice, ...
October 2007
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Handwritten Signatures Authentication using ANNs Committee MachinesM.Heinen, F. Osório and P. Engel
1. Motivation and Context
Motivation: Security
How to guarantee a legitimate access to resources:Data access, Bank access, Passport, Credit Card, etc.
Do you really are who you claim to be ??
Context: Biometric Systems Signature
Identify: who you are Off-LineVerify: authenticate one user Scanned Signature (static)
Physical features: On-Line Fingerprints, hand, eyes, ... Digital Pen (dynamic)Behavioral features: Signature, keyboard typing, voice, ...
October 2007
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Handwritten Signatures Authentication using ANNs Committee MachinesM.Heinen, F. Osório and P. Engel
1. Motivation and Context
Motivation: Security
How to guarantee a legitimate access to resources:Data access, Bank access, Passport, Credit Card, etc.
Do you really are who you claim to be ??
Context: Biometric Systems Signature
Identify: who you are Off-LineVerify: authenticate one user Scanned Signature (static)
Physical features: On-Line Fingerprints, hand, eyes, ... Digital Pen (dynamic)Behavioral features: Signature, keyboard typing, voice, ...
ThisWork
October 2007
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Handwritten Signatures Authentication using ANNs Committee MachinesM.Heinen, F. Osório and P. Engel
2. Handwritten Signature Authentication
Signature Authentication
Binary Classification:- True: Authentic [ not exactly the same as a previously known ]- False: Random, Traced, Skilled
On-Line Signatures
- Dynamical and Temporal Information- Easy to access input devices... tablet + pen Becoming more usual nowadays:
Tablet PCsPalm TopsCell phonesPortable GamesLow Cost Digitizing Tables
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Handwritten Signatures Authentication using ANNs Committee MachinesM.Heinen, F. Osório and P. Engel
3. NeuralSignX System
NeuralSignX Modules:
1. Acquisition Module
2. Pre-Processing Module
3. ANN Module
- Learning
- Classification
October 2007
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Handwritten Signatures Authentication using ANNs Committee MachinesM.Heinen, F. Osório and P. Engel
3. NeuralSignX System
NeuralSignX Modules:
1. Acquisition Module
2. Pre-Processing Module
3. ANN Module
- Learning
- Classification
LOGIN=MILTONLOGIN=MILTON116 478 0 21:42:23:821116 478 0 21:42:23:821116 478 1 21:42:23:831116 478 1 21:42:23:831125 467 1 21:42:23:842125 467 1 21:42:23:842134 456 1 21:42:23:852134 456 1 21:42:23:852145 441 1 21:42:23:873145 441 1 21:42:23:873168 444 0 21:42:23:883168 444 0 21:42:23:883198 373 1 21:42:23:894198 373 1 21:42:23:894
Position XPosition YPen Up/DownTime Stamp
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Handwritten Signatures Authentication using ANNs Committee MachinesM.Heinen, F. Osório and P. Engel
3. NeuralSignX System
NeuralSignX Modules:
1. Acquisition Module
Collected Data...
2. Pre-Processing Module
3. ANN Module
- Learning
- Classification
Signatures Database
TOTAL: 2440 Signatures
1800 authentic signatures (30 per user, 60 users)320 false (traced forgeries)320 false (skilled forgeries)
Select 1 user to authenticateAll other signatures can be used as non-authentic
The user signatures are divided into: training set / validation setTypical authentic user training data set is composed by 10 to 15 signatures
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Handwritten Signatures Authentication using ANNs Committee MachinesM.Heinen, F. Osório and P. Engel
3. NeuralSignX System
NeuralSignX Modules:
1. Acquisition Module
2. Pre-Processing Module
3. ANN Module
- Learning
- Classification
Position and Scale Adjust
October 2007
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Handwritten Signatures Authentication using ANNs Committee MachinesM.Heinen, F. Osório and P. Engel
3. NeuralSignX System
NeuralSignX Modules:
1. Acquisition Module
2. Pre-Processing Module
3. ANN Module
- Learning
- Classification
Features Extraction
- Signature elapsed time (1 value)- Number of pen lifts (1 value)- Signature length (1 value)- Medium and maximum pen velocity (2 values)- Number of pen X, Y direction changes (2 values)- Cardinal points measure (8 values)- Pseudo-vectors total length (8 values)- Signature density grid (48 values)- Vertical and horizontal intersections (26 values)- Sequential sampling (16 values)- Symmetry measure (2 values)
Total: 117 values! (or even more)
October 2007
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Handwritten Signatures Authentication using ANNs Committee MachinesM.Heinen, F. Osório and P. Engel
3. NeuralSignX System
NeuralSignX Modules:
1. Acquisition Module
2. Pre-Processing Module
3. ANN Module
- Learning
- Classification
Features Extraction
- Signature elapsed time (1 value)- Number of pen lifts (1 value)- Signature length (1 value)- Medium and maximum pen velocity (2 values)- Number of pen X, Y direction changes (2 values)- Cardinal points measure (8 values)- Pseudo-vectors total length (8 values)- Signature density grid (48 values)- Vertical and horizontal intersections (26 values)- Sequential sampling (16 values)- Symmetry measure (2 values)
Total: 117 values! (or even more)
Large Input Space:Reduced using PCA and other techniques presented in some of our previous worksIEEE/IJCNN 2006, SBIA/SBRN/WCI 2006 and SBC/ENIA 2005
October 2007
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Handwritten Signatures Authentication using ANNs Committee MachinesM.Heinen, F. Osório and P. Engel
3. NeuralSignX System
NeuralSignX Modules:
1. Acquisition Module
2. Pre-Processing Module
3. ANN Module
- Learning
- Classification
Features Extraction
- Signature elapsed time (1 value)- Number of pen lifts (1 value)- Medium and maximum pen velocity (2 values)- Cardinal points measure (8 values)- Signature density grid (48 values)- Vertical and horizontal intersections (26 values)
Total: 86 input values
In this work:we selected these 6 features represented by 86 values
NeuralsignX pre-processing module is available at:http://inf.unisinos.br/~osorio/NeuralSignX/nsx.html
October 2007
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Handwritten Signatures Authentication using ANNs Committee MachinesM.Heinen, F. Osório and P. Engel
3. NeuralSignX System
NeuralSignX Modules:
1. Acquisition Module
2. Pre-Processing Module
3. ANN Module ANN - Artificial Neural Networks
Neural Learning:Neural Learning: Select 1 user as authentic
Learn how to classify as
- Authentic Signature
- Non Authentic Signature
Classification:Classification: Committee of three specialists (5 Neural Nets each)
Inputs: 86 valuesOutput: 1 binary (Authentic or Not)
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Handwritten Signatures Authentication using ANNs Committee MachinesM.Heinen, F. Osório and P. Engel
3. NeuralSignX System
NeuralSignX Modules:
1. Acquisition Module
2. Pre-Processing Module
3. ANN Module
- Learning: > MLP with RProp
- Classification: Main goals
> Improve generalization
> Reduce FP (False Positive)
FP: False Positive - False signature classified as authentic (accepted)
FN: False Negative - Authentic signature classified as false (refused)
Inputs: 86 values are divided into 3 Esps
Output: 1 combined binary output
Simulation Tool: SNNS Simulator
Committee of three specialists
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Handwritten Signatures Authentication using ANNs Committee MachinesM.Heinen, F. Osório and P. Engel
4. Description of Experiments
Experiments:
Signatures Database
1800 Authentic
320 Traced
320 Skilled=================
2440 Signatures (usually from 10 to 30 different signatures per user)
10 Users were authenticated10 Folds Cross-Validation 10 Runs each folder (≠ initializations)1000 experiments
Results:HIT = % of correct answerFPR = False Positive Rate (accepted)FNR = False Negative Rate (refused)
Single ANN: 86 inputs, 5 hidden, 1 output versusCommittee: 3 specialists (5 ANN in each one)
Experiment Results:
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Handwritten Signatures Authentication using ANNs Committee MachinesM.Heinen, F. Osório and P. Engel
5. Signature Authentication Results
Results: One Single ANN
MSE = Mean Squared ErrorHIT = % of correct answerFPR = False Positive Rate (accepted)FNR = False Negative Rate (refused)
Top:
FPR = 0.14 %FNR = 23.00 %
Global mean
HIT = 99.86 %FPR = 0.05 %FNR = 8.03 %
Single ANN
High FNR error rate
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Handwritten Signatures Authentication using ANNs Committee MachinesM.Heinen, F. Osório and P. Engel
5. Signature Authentication Results
Results: Specialists committee
MSE = Mean Squared ErrorHIT = % of correct answerFPR = False Positive Rate (accepted)FNR = False Negative Rate (refused)
Top:
FPR = 0.00 %FNR = 13.33 %
Global mean
HIT = 99.95 %FPR = 0.00 %FNR = 4.00 %
Committee
Lower FNR error rate
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Handwritten Signatures Authentication using ANNs Committee MachinesM.Heinen, F. Osório and P. Engel
5. Signature Authentication Results
Comparing Results: Single x Specialists committee
MSE = Mean Squared ErrorHIT = % of correct answerFPR = False Positive Rate (accepted)FNR = False Negative Rate (refused)
Top:
FPR = 0.00 %FNR = 13.33 %
Global mean
HIT = 99.95 %FPR = 0.00 %FNR = 4.00 %
Committee
Top:
FPR = 0.14 %FNR = 23.00 %
Global mean
HIT = 99.86 %FPR = 0.05 %FNR = 8.03 %
Single ANN
Specialists committee performs better!
October 2007
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Handwritten Signatures Authentication using ANNs Committee MachinesM.Heinen, F. Osório and P. Engel
6. Conclusions
Conclusions and Future Work
We proposed: Authentication using ANN Committee Machines
We obtained: a more robust and secure authentication system
- Pre-Processing: Good Features = Good Results- Classification: Specialist Committee are able to... Improve Generalization (HIT) ! Reduce Errors (FPR and FNR) !
Future Work: How to Improve Security ? Hybrid User Authentication System
"Combine SpecialistsCombine Specialists but also combine different biometric techniques"combine different biometric techniques"
Composed authentication: Signature, Eye scanning, Fingerprints, etc.
October 2007
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Handwritten Signatures Authentication using ANNs Committee MachinesM.Heinen, F. Osório and P. Engel
CONTACT INFORMATION
UNISINOS University - BrazilUNISINOS University - Brazil
Applied Computing Research Post-grad ProgramApplied Computing Research Post-grad Program - PIPCA - PIPCA
Artificial Intelligence Research GroupArtificial Intelligence Research Group - - GIAGIA
UFRGS University - BrazilUFRGS University - Brazil
Informatics Institute - IIInformatics Institute - II
NeuralSignX Project Web Page:NeuralSignX Project Web Page:
http://inf.unisinos.br/~osorio/NeuralSignX/nsx.htmlhttp://inf.unisinos.br/~osorio/NeuralSignX/nsx.html
Contact:
Milton Heinen, Fernando Osório and Paulo Engel
Web: http://inf.unisinos.br/~osorio/
E-Mail: [email protected]