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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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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