fusion of hmm s likelihood and viterbi path for on line signature verification
Download
Skip this Video
Download Presentation
Fusion of HMM’s Likelihood and Viterbi Path for On-line Signature Verification

Loading in 2 Seconds...

play fullscreen
1 / 22

Fusion of HMM’s Likelihood and Viterbi Path for On-line Signature Verification - PowerPoint PPT Presentation


  • 114 Views
  • Uploaded on

Fusion of HMM’s Likelihood and Viterbi Path for On-line Signature Verification. Bao Ly Van - Sonia Garcia Salicetti - Bernadette Dorizzi Institut National des Télécommunications. Presented by Bao LY VAN. Prague – May 2004. Overview. HMM for Online Signature

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Fusion of HMM’s Likelihood and Viterbi Path for On-line Signature Verification' - jace


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
fusion of hmm s likelihood and viterbi path for on line signature verification

Fusion of HMM’s Likelihood and Viterbi Path for On-line Signature Verification

Bao Ly Van - Sonia Garcia Salicetti - Bernadette Dorizzi

Institut National des Télécommunications

Presented by Bao LY VAN

Prague – May 2004

overview
Overview
  • HMM for Online Signature
  • Likelihood Approach: Normalized Log-Likelihood information given by the HMM
    • Comparison with Dolfing’s system on Philips database

[Ref] J.G.A. Dolfing, "Handwriting recognition and verification, a Hidden Markov approach", Ph.D. thesis, Philips Electronics N.V., 1998.

  • Viterbi Path Approach: exploit the Viterbi Path information given by the HMM
    • Motivation of the Viterbi Path approach
    • Fusion Likelihood and Viterbi Path
  • Experiments & Results

New

introduction of online signature
Azimuth (0°-359°)

Altitude (0°-90°)

270°

180°

90°

Introduction of Online Signature
  • Captured by a Digitizing Tablet
  • A signature: a sequence of sampled points
    • Raw data:
      • Coordinates: x(t), y(t)
      • Pressure: p(t)
      • Pen Inclination Angles
hmm architecture
HMM Architecture
  • Continuous, left-right HMM
  • Mixture of 4 Gaussians
  • Personalized number of states
    • 30 points to estimate a gaussian

When using 5 training signatures, the personalized

number of states for this signer is 10

feature extraction
Feature Extraction
  • Features extracted from coordinates
    • Velocity
    • Acceleration
    • Curvature radius
    • Normalized coordinates by the gravity center
    • Length to Width ratio
    • ...
  • 25 features at each point of the signature:signature = sequence of feature vectors
personalized feature normalization
Feature A

Feature A

Normalize

Feature Z

Feature Z

Personalized Feature Normalization
  • Goals:
    • Same variance for all features = same importance
    • A good choice of leads to a faster convergence
    • Avoid the overflow problem in training phase
  • Implementation:
    • Normalization factors (one per feature) of each signer are stored with his/her signature model (HMM)
    • A test signature will be normalized according to these factors
hmm likelihood approach
HMM Likelihood Approach
  • Log-Likelihood of a signature
    • Normalized by the signature length
  • Score
    • Based on the Distance between the LLN of the test signature and the Average LLN of training signatures: |LLN-LLNmean|
  • Convert to similitude between [0, 1]
  • (Likelihood Score)
what is the viterbi path approach
NewWhat is The Viterbi Path Approach?
  • VP is the sequence of states that maximizes the likelihood of the test signature

Normalized

Log-Likelihood

HMM

(Viterbi Algorithm)

input

output

Signature

Viterbi Path (VP)

representation of viterbi path
Representation of Viterbi Path
  • VP generated by a N states HMM is represented by a N components Segmentation Vector (SV)
  • Each component of SV contains the number of points modeled by the corresponding state
complementarity between vp and ll
LL = -1166.10

LLN = -14.95

SV = (21, 30, 27)

LL = -296.46

LLN = -16.47

SV = (18, 0, 0)

Complementarity between VP and LL
  • Genuine and forged signatures can have very close Normalized Log-Likelihoods although their VPs (SVs) are quite different
  • It is easier to forge the system when the score based on Normalized Likelihood
how to use the vp sv information
Hamming Distance

HMM

Hamming Distance

SV 1

Test Signature

Training Signature 1

...

SV 2

Training Signature 2

Hamming Distance

SV

SV K

Training Signature K

References

How to use the VP (SV) information?
  • SVsof HMM’s training signatures are saved as References
  • Convert Average Distance to similitude between [0, 1] (Viterbi Score)

average

AverageDistance

viterbi score vs likelihood score
Viterbi Score vs Likelihood Score
  • Important overlap when using only one score
  • Viterbi and Likelihood scores are complementary
  • Simple arithmetic mean is used for fusion (no extra-training)
experiments overview
Experiments Overview
  • Protocol P1:
    • Exploits only the likelihood score on Philips database (with the same protocol as Dolfing)

[Ref] J.G.A. Dolfing, "Handwriting recognition and verification, a Hidden Markov approach", Ph.D. thesis, Philips Electronics N.V., 1998.

  • Protocol P2:
    • Performs fusion of 2 scores on Philips database
  • Protocol P3:
    • Performs fusion of 2 scores on BIOMET database
p1 likelihood score on philips database
NN

0.7

1

1.3

1.6

2

2.5

3.2

6

10

TE min(%)

1.32

1.59

0.97

0.92

0.88

0.97

1.10

1.23

1.98

1.98

EER (%)

1.35

2.04

1.02

0.96

0.95

1.03

1.13

1.24

1.99

2.02

P1: Likelihood Score on Philips Database
  • 15 signatures to train HMM
  • Repeat 10 times: robust results
  • Our result is of 0.95% EER compared to 2.2% EER of Dolfing (1998)
slide15
Likelihood

Viterbi Path

Fusion

TE min (%)

3.73

7.66

3.26

EER (%)

4.18

8.12

3.54

P2: Fusion on Philips database

  • Only 5 signatures to train HMM
  • Repeat 50 times: robust results
  • Fusion lowers the Error Rate by 15% (compared to likelihood)
slide16
genuine test data

Likelihood

Viterbi Path

Fusion

No time variability

TE min (%)

5.27

3.71

2.47

EER (%)

6.45

4.07

2.84

Time variability

(5 months before)

TE min (%)

14.30

7.44

6.95

EER (%)

16.70

9.21

8.57

P3: Fusion on BIOMET database

  • 5 signatures to train HMM
  • Genuine test on two session
  • Repeat 50 times: robust results
  • Fusion lowers the Error Rate by a factor 2 (compared to likelihood)
conclusions
Conclusions
  • We have built a HMM-based system and introduced 2 measures of information:
    • Likelihood score
    • Viterbi score
  • We have compared both scores on two databases: Philips and BIOMET
  • The new approach using VP information can give better results than LL approach (BIOMET)
  • Fusion of both scores improves results which shows their complementarity
protocol 1 only likelihood
NN

0.7

1

1.3

1.6

2

2.5

3.2

6

10

TE min(%)

1.32

1.59

0.97

0.92

0.88

0.97

1.10

1.23

1.98

1.98

EER (%)

1.35

2.04

1.02

0.96

0.95

1.03

1.13

1.24

1.99

2.02

  • Mean result of 10 trials
Protocol 1: Only Likelihood
  • Philips database
    • 51 signers, 30 genuine and about 70 forgeries per signer
    • Forgery of high quality
  • Dolfing’s protocol
    • 15 genuine signatures to train HMM
    • 15 other genuine signatures and forgeries to test HMM (~4000 signatures)
    • Fixed partition of training and testing genuine signatures
  • Our result is of 0.95% EER compared to 2.2% EER of Dolfing (1998)
slide21
Likelihood

Viterbi Path

Fusion

TE min (%)

3.73

7.66

3.26

EER (%)

4.18

8.12

3.54

Protocol 2: Fusion on Philips database

  • Protocol
    • Only 5 signatures to train HMM, randomly selected from 30
    • Test on the remaining 25 genuine signatures and forgeries
    • Repeat 50 times: robust results
  • Fusion lowers the Error Rate by 15% (compared to likelihood)
slide22
genuine test data

Likelihood

Viterbi Path

Fusion

2nd session

TE min (%)

5.27

3.71

2.47

EER (%)

6.45

4.07

2.84

1st session

(5 months before)

TE min (%)

14.30

7.44

6.95

EER (%)

16.70

9.21

8.57

Protocol 3: Fusion on BIOMET

  • BIOMET Database
    • 87 signers
    • Two sessions spaced of 5 months: 5 + 10 genuine, 12 forgeries per signer
  • Protocol:
    • 5 signatures (2nd session) to train HMM, randomly selected from 10
    • test on the remaining 5 genuine signatures of the 2nd session, on the 5 genuine of the 1st session and the forgeries
    • Repeat 50 times: robust results
  • Fusion lowers the Error Rate by a factor 2 (compared to likelihood)
ad