S. Y. Kung1 and M. W. Mak2 - PowerPoint PPT Presentation

Title: S. Y. Kung1 and M. W. Mak2


1
On Consistent Fusion of Multimodal Biometrics

• S. Y. Kung1 and M. W. Mak2
• 1Dept. of Electrical Engineering, Princeton
  University
• 2Dept. of Electronic and Information Engineering,
  
• The Hong Kong Polytechnic University

2
Outline

• Why Fusion for Audio-Visual Biometrics
• Consistent (vs. Catastrophic) Fusion
• Mixture-of-Expert Fusion Architecture
• Consistent fusion
• Linear fusion
• Nonlinear fusion
• Conclusion

3
Why Fusion for Audio-Visual Biometrics

• Voice biometrics can suffer severe performance
  degradation under noisy environment, but facial
  images are unaffected.
• Facial image quality can be severely affected in
  poor lighting conditions, but lighting has no
  effect on voice quality.
• Speech and faces provide complementary
  information sources that are ideal candidates for
  fusion as verified by ROC(DET).
• Results based on 295 subjects from XM2VTSDB

4
Mixture-of-Expert Fusion Architecture

• The lower layer contains local experts, each
  produces a local score based on a single modality
• The upper layer contains a gating network

5
ROC(DET)

• We may consider the audio and visual sources
  separately, i.e., we have two decision thresholds
  and two decision boundaries.
• By shifting the decision boundaries
  independently, we obtain two DET curves, one for
  each modality.

False Rejection Rate
False Acceptance Rate
6
Regions of Consistent and Catastrophic Fusion
Catastrophic Region
Consistent Region
7
Consistent Fusion

• Yield a lower bound performance of consistent
  fusion (fusion that leads to performance equal to
  or better than any individual modalities)

1
2
3
4
5
Voice
1
False Rejection Rate
2
users
3
4
5
Face
6
7
5
8
6
7
9
8
Imposters
9
False Acceptance Rate
8
Linear Fusion
False Rejection Rate
False Acceptance Rate
9
Nonlinear Fusion

• Score distribution of multi-modalities

10
Nonlinear Fusion
False Rejection Rate
False Acceptance Rate
11
Linear Vs. Nonlinear Fusion
Voice
1
2
3
4
5
Face
6
7
FaceVoice (Linear)
8
9
FaceVoice (Nonlinear)
12
What if there are N (N gt2) modalities

• Which pair of modalities would be the best
  choice?
• Answer DET (ROC) could provide a good
  indication on
• (1) how good and (2) how
  complementary.
• What guaranteed advantage to adopt N (Ngt2)
  modalities?

False Rejection Rate
False Acceptance Rate
13
But there is a catch on statistical significance!

• This can be upheld only if the
• training data set,
• held-out set, and
• test set
• are assumed to have statistically the same
  distribution and provided in large volume.

14
Thank you
15
Conclusions

• The notion of consistent fusion is proposed for
  multimodality fusion
• The consistent fusion framework leads to several
  adaptive fusion schemes, such as hard-switching,
  linear combination, and adaptive nonlinear SVM
  fusion.
• Results suggest that consistent fusion provides a
  valuable framework for choosing different
  modalities in multimodal biometric authentication.

16
Score Distributions of Single Modality

• For a single modality, a test sequence from
  a claimant is classified as coming from the true
  client if

Decision threshold
17
DET Based on Single Modality

• Changing the threshold ?from small to large
  values, we obtain an ROC or DET

Large ?
False Rejection Rate
Small ?
False Acceptance Rate
18
Is Linear Fusion a good idea?
19
Why Fusion for Audio-Visual Biometrics
Fused Score
Adaptive Gating Network (e.g. hard-switch, linear
combiner, and SVM)
Classifier for Audio Channel
Classifier for Visual Channel