The document discusses a multimodal biometric system that integrates fingerprint verification, face recognition, and speaker verification to improve performance in user identification. It outlines the components and processes of the system, including data acquisition, template matching, and decision fusion. The findings suggest that this integration enhances verification accuracy compared to individual biometric methods.

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2. Content
 Introduction.
 Multimodal Biometric System.
 Verification module.
 Fingerprint verification
 Face recognition
 Speaker verification
 Decision fusion.
 Performance Evaluation
 Databases
 Benchmarks
 Conclusions
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3. Introduction
 Biometric system is often not able to meet the desired
performance requirements.
 In order to enable a biometric system to operate
effectively in different applications and environments,
a multimodal biometric system is preferred.
 In this paper introduce a multimodal biometric system
which integrates fingerprint verification , face
recognition and speaker verification.
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4. Introduction
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This system take the advantage of the capabilities of each individual biometrics

5. Introduction
 System consist of four components:
1. Acquisition module
2. Template database
3. Enrollment module
4. Verification module
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Acquisition module Enrollment module
Verification module
Template
Database

7. Content
 Introduction.
 Multimodal Biometric System.
 Verification module.
 Fingerprint verification
 Face recognition
 Speaker verification
 Decision fusion.
 Performance Evaluation
 Databases
 Benchmarks
 Conclusions
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8. Formulation
Database contain templates : Φ1
, Φ2
,…., Φ 𝑁
Template of user 𝑖 : Φ𝑖
= {Φ𝑖
1 , Φ𝑖
2 , Φ𝑖
3}
Input : Φ0
, 𝐼
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fingerprint face speech
{Φ0
1 , Φ0
2 , Φ0
3} User identity

9. Formulation
𝐼 ∈
𝑤1 , 𝑖𝑓 𝐹 Φ0 , Φ 𝐼 > 𝜖
𝑤2 , 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
𝐹 : is the a measuring similarity function
I belong to 𝑤1 : if genuine (true)
I belong to 𝑤2 : if imposter (false)
𝜖 : threshold
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10. Content
 Introduction.
 Multimodal Biometric System.
 Verification module.
 Fingerprint verification
 Face recognition
 Speaker verification
 Decision fusion.
 Performance Evaluation
 Databases
 Benchmarks
 Conclusions
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11. Multimodal Biometric System
 The verification process consists of four stages:
1. Fingerprint verification
2. Face recognition
3. Speaker verification
4. Decision fusion
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12. Fingerprint Verification
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13. Fingerprint Verification
 Fingerprint is the pattern of ridges.
 The two most prominent ridge characteristics, called
minutiae features, are: Ridge ending and Ridge
bifurcation.
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Ridge ending Bifurcation

14. Fingerprint Verification
Steps:
 Minutiae extracting : extract minutiae from input finger
print images.
 Minutiae matching : determine the similarity of two
minutiae patterns.
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15. Fingerprint Verification
𝐹1 Φ0
1, Φ 𝐼
1 =
100𝐶2
𝑃𝑄
 𝐶 : total number of corresponding minutiae pairs
between Φ0
1, Φ 𝐼
1
 𝑃 : total number of minutiae in Φ0
1
 𝑄 : total number of minutiae in Φ 𝐼
1
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16. Face Recognition
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17. Face Recognition
There are two major tasks:
 Face location : finds if there is a face in the input image.
 Face recognition : finds the similarity between the
located face and the stored templates.
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18. Face Recognition
 In our system : eigenface approach is used.
 The eigenface-based face recognition method is
divided into two stages:
1. Training stage.
2. Operational stage.
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19. Face Recognition
1. Training stage :
 set of orthonormal images that best describe the
distribution of the training facial image in a lower
dimensional subspace (eigenspace) is computed.
 The training facial images are projected onto eigenspace
to generate the representations of the facial images in the
eigenspace.
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20. Face Recognition
2. Operational stage : detected facial image is projected
onto the same eigenspace , and the similarity between the
input facial image and the template is computed in the
eigenspace.
𝐹2 Φ0
2, Φ 𝐼
2 = − Φ 𝐼
2 − Φ0
2
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21. Speaker Verification
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22. Speaker Verification
 Text-dependent system : system knows text spoken by
user.
 Uses left to right Hidden Markov Model (HMM) of the
10th order linear prediction coefficients (LPC) of the
cepstrum to make a verification.
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23. Speaker Verification
𝐹3 Φ0
3, Φ 𝐼
3 = max
𝑖1……𝑖 𝐿
{
𝑘=1
𝐿
𝑞 𝑠𝑖𝑘|𝑡𝑖𝑘 𝑝 𝑡𝑖𝑘|𝑡𝑖𝑘−1 }
 L : feature vector length.
 𝑞 𝑠𝑖𝑘|𝑡𝑖𝑘 : probability of transition to visible state
depends on current hidden state.
 𝑝 𝑡𝑖𝑘|𝑡𝑖𝑘−1 : probability of a state for each time step
depend only on the previous state
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24. Decision Fusion
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25. Decision Fusion
 The final decision made by our system is based on the
integration of the decision made by the tree
biometrics.
 The output of each module is a similarity value.
 𝑋1, 𝑋2, 𝑋3 are variables used to indicate the similarity
between input and template.
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26. Decision Fusion
 Classification:
𝑋1
0
, 𝑋2
0
, 𝑋3
0
∈
𝑤1 , 𝑖𝑓
𝑝1 𝑋1
0
, 𝑋2
0
, 𝑋3
0
𝑤1)
𝑝2 𝑋1
0
, 𝑋2
0
, 𝑋3
0
𝑤2)
> λ
𝑤2 , 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
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27. Content
 Introduction.
 Multimodal Biometric System.
 Verification module.
 Fingerprint verification
 Face recognition
 Speaker verification
 Decision fusion.
 Performance Evaluation
 Databases
 Benchmarks
 Conclusions
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28. Database
 A training database of 50 users was collected.
 For each user :
o 10 fingerprint images using optical fingerprint scanner.
o 9 face images using Panasonic video camera.
o 12 speech samples using Laptec microphone.
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29. Benchmark
 In out test, a total of 36,796 impostor and 358 genuine
were generated and tested.
 We can conclude that the integration of fingerprint ,
face and speech leads to an improvement in
verification performance.
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30. Benchmark (ROC)
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false acceptance rate
authenticateacceptancerate

31. Content
 Introduction.
 Multimodal Biometric System.
 Verification module.
 Fingerprint verification
 Face recognition
 Speaker verification
 Decision fusion.
 Performance Evaluation
 Databases
 Benchmarks
 Conclusions
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32. Conclusion
 Multimodal biometric technique which combines multiple
biometrics in making a personal identification can be used to
overcome the limitations of individual biometrics.
 If a user can not provide a good fingerprint images ( due to dry
fingers , cuts, etc.) then face and voice may be better biometric
indicators.
 These biometrics indicators complement one another in their
advantages and strengths.
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