Pattern recognition multi biometrics using face and ear seminar Mustansiriya University Department of Education Computer Science

1. Multibiometrics Using
Face and Ear
Shahad

2. Outlines
 Introduction
 Multimodal biometrics systems
 Scenarios in a multimodal biometric system
 Reasons to combining
 Project Goals
 Face Biometrics
 Ear biometrics
 Implementation
 Fusion
 Advantages of Multi-modal Biometrics
 Disadvantages of Multi-modal Biometrics
 Conclusion

3. Introduction
Unimodal biometrics has several problems such as:
 Noisy data.
Intra class variation, feature sets of a same individual
Inter class similarities, feature sets belonging to
different individuals
Non universality.
 Spoofing.
which cause this system less accurate and secure.

4. Multimodal biometrics systems
improve performance
A combination in a verification system
 improves system accuracy
A combination in an identification system improves
system speed as well as accuracy
A combination of uncorrelated modalities (e.g. fingerprint
and face, two fingers of a person, etc.) is expected to
result in a better improvement in performance than a
combination of correlated modalities (e.g. different
fingerprint matchers)

5. When designing a multi-biometric
things must be considered:
1. The biometric features to use
2. The methods of evaluation
3. The level of fusion
4. How it will be performed, .
5. Cost
6. Operational Mode
7. Multimodal Database

6. Examples of multi-biometrics
systems.

7. Scenarios in a multimodal
biometric system
 Multiple sensors : multiple sensors are used to sense the
same biometric identifier.
 Multiple Biometrics : sense different biometric identifiers.
Multiple Units : fingerprints from two or more fingers.
Multiple Snapshots : more than one instance of the same
biometric.
 Multiple Matching algorithm : combines different
representation and matching algorithms

8. Reasons to combining
(a) the ear is part of the face
(b) the ear can be acquired using the same sensor
as the face
(c) the same type of feature extraction and
matching algorithms can be used for both

9. Project Goals
To use both face and ear recognition techniques in
a single tool for enhanced security and flexibility
Compare the performance of both biometrics
Identify common sources of errors for both
techniques

10. Face Biometrics
Passive physiological method
Natural method – humans recognize people by
looking at their faces
Fast development of new algorithms
Still many unsolved problems including compensation
of illumination changes and pose invariance
Some popular methods
2D- 3D

11. EAR BIOMETRICS
 Human ears have been used as major feature in the
forensic science for many years.
Ear prints found on the crime scene have been used as a
proof in over few hundreds cases in the Netherlands and
the United States.
Human ear contains large amount of specific and unique
features that allows for human identification.
 Ear images can be easily taken from a distance and
without knowledge of the examined person.
Therefore suitable for security, surveillance, access
control and monitoring applications.

12. EAR BIOMETRICS
 Ear does not change during human life, and face changes
more significantly with age than any other part of human
body
 Colour distribution is more uniform in ear than in human
face.
 Not much information is lost while working with the
greyscale or binarized images
 Ear is also smaller than face , which means that it is
possible to work faster and more efficiently with the images
with the lower resolution
 Ear images cannot be disturbed by glasses, beard nor
make-up. However, occlusion by hair or earrings is
possible Ear images can be easily taken from a distance
and without knowledge of the examined person

13. Implementation
Acquiring an image of the subject from
scanner/digital camera/video recording
Detect the location of any face or ear in the image
Analysis of the spatial geometry of distinguishing
features of face/ear and generate a template
Compare the template with those in the database of
known faces/ears
Declare match or mismatch depending on the
similarity and security configuration

14. Fusion
 Multimodal biometric systems integrate information
presented by multiple biometric indicators. The
information can be consolidated at various levels.
 Fusion is divided into three parts.
1. Fusion at the feature extraction level before matching
2. Fusion at the matching score (confidence or rank)
level.
3. Fusion at the decision (abstract label) level

16. Feature Level Fusion
Combining feature vectors
Fusion at feature level is expected to provide better
recognition results but it has also observed that when
features of different modalities are compatible with each
other then fusion at feature level achieves more accuracy

17. Matching Score Level Fusion
 Feature vectors are processed separately and individual matching score
is found and finally these matching scores are combined to make
classification.
 One important aspect has to be addressed in the matching score level is
the normalization of scores obtained from multiple modalities

18. Decision Level Fusion
 Each biometric system makes its own recognition
decision based on its own feature vector.

19. Advantages of Multi-modal Biometrics
• More accurate.
• Multibiometric systems can effectively address the problem
of noisy data.
• Multibiometric systems can be effectively used in tracking
or continuous monitoring system where only one trait is not
sufficient
• A multibiometric system may also be viewed as a
fault tolerant system.
• Multibiometric systems can offer substantial improvement
in the matching accuracy.
• Multibiometric system makes the life of any impostor
harder.
• Multibiometric systems provide better recognition
performance.

20. Disadvantages of Multi-modal
Biometrics
 High cost.
 High enrolment time.
 High transit times.
 Increase system development and complexity.
Reduced Matching Level: if a stronger biometric is used
with a weaker biometric, the result is not a stronger
combined system. The error rate of the weaker biometric
can bring down the overall effectiveness of the system

21. Conclusion
1.Accurate more accurate(multi biometric)
2.life cycle database need to update for face recognition .
database for ear recognition not need to be update.
3.Community acceptance acceptance
4.Ability to applied easy of implementation
5.Cost high
6. Physiological and/or behavioral characteristics.
physiological

22. 7.Automatic real time System is online
8.maintenance requirement
Maintenance for hardware and software(for face
recognition)
9.Authentication: It is authenticate because is it not use
password(my be forget ) or card ( my be loss) .
10.Identification : It Identification from the face .

23. Thanks for Listening