Multimodal Biometric Systems
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This document summarizes a seminar presentation on multimodal biometric systems. It discusses the limitations of unimodal biometric systems and how combining multiple biometric traits can improve accuracy. It covers classification of multimodal systems based on architecture, sources, fusion level, and methodology. Score normalization and different fusion techniques at the sensor, feature, matching score, and decision levels are also summarized. The conclusion states that multimodal biometrics provides higher security than unimodal systems through appropriate normalization and fusion methods.
![Continue
(5) Susceptibility to circumvention:Although it is
very difficult to steal someone’s biometric traits, it is still
possible for an impostor to circumvent a biometric system using
spoofed traits.
Behavioral traits like signature and voice are more susceptible
to such attacks than physiological traits.
TEST TEST FALSE FALSE
PARAMETER REJECT RATE ACCEPT RATE
Fingerprint FVC[2004] 20 years 2% 2%
(average age)
Face FRVT[2002] Varied lighting 10% 1%
Outdoor/indoor
Voice NIST[2000] Text 10-20% 2-5%
Independent](https://image.slidesharecdn.com/multimodalbiometricsystems-130128005332-phpapp02/85/Multimodal-Biometric-Systems-6-320.jpg)
![Score Normalisation
• Scores output by individual matchers:
– Non-homogeneous: distance or similarity
– Ranges may be different; e.g., [0,100] or [0,1000]
– Distributions may be different
• To facilitate fusion:
– Modify the location and scale parameters of score
distributions of individual matchers.
– Apply transformation to scores present in the genuine
impostor overlap region.](https://image.slidesharecdn.com/multimodalbiometricsystems-130128005332-phpapp02/85/Multimodal-Biometric-Systems-19-320.jpg)
![Normalization Techniques
Min-Max(MM):This method maps the raw scores to the [0, 1]
range . The quantities max(S) and min(S) specify the end points of
the score range:
S:set of all scores for that matcher
s:a raw matching score
Z Score(ZS):
Tanh(TH):
It maps the raw scores to the (0, 1) range](https://image.slidesharecdn.com/multimodalbiometricsystems-130128005332-phpapp02/85/Multimodal-Biometric-Systems-20-320.jpg)
![• Adaptive(AD): The errors of individual biometric matchers stem
from the overlap of the genuine and impostor score distributions. This
overlap region represented by its center c and its width w. To decrease
the effect of this overlap on the fusion algorithm, an adaptive
normalization procedure apply and aims to increase the separation of the
genuine and impostor distributions, while still mapping the scores to [0,1]
range.](https://image.slidesharecdn.com/multimodalbiometricsystems-130128005332-phpapp02/85/Multimodal-Biometric-Systems-21-320.jpg)
Multimodal Biometric Systems
- 1. NATIONAL INSTITUTE OF TECHNOLOGY,ROURKELA Seminar on Multimodal Biometric Systems Presented By: Rajendra Prasad Nayak
- 2. CONTENT • WHY MULTIMODAL BIOMETRICS? • CLASSIFICATION • SCORE NORMALIZATION • BIOMETRIC FUSION • CONCLUSION
- 3. Why multimodal biometrics? • Unimodal biometric systems perform person recognition based on a single source of biometric information. • Such systems are often affected by the following problems: (1) Noisy sensor data: Noise can be present in the acquired biometric data mainly due to defective or improperly maintained sensors. .
- 4. Continue (2) Non-universality: If every individual in the target population is able to present the biometric trait for recognition, then the trait is said to be universal. However, not all biometric traits are truly universal. people with hand-related disabilities, manual workers with many cuts and bruises on their fingertips, and people with very oily or dry fingers NIST reported 2% people cannot enroll using finger print.
- 5. Continue (3) Lack of individuality : Features extracted from biometric characteristics of different individuals can be quite similar. A small proportion of the population can have nearly identical facial appearance due to genetic factors (e.g., father and son, identical twins, etc.) (4) Lack of invariant representation :The biometric data acquired from a user during verification will not be identical to the data used for generating the user’s template during enrollment.
- 6. Continue (5) Susceptibility to circumvention:Although it is very difficult to steal someone’s biometric traits, it is still possible for an impostor to circumvent a biometric system using spoofed traits. Behavioral traits like signature and voice are more susceptible to such attacks than physiological traits. TEST TEST FALSE FALSE PARAMETER REJECT RATE ACCEPT RATE Fingerprint FVC[2004] 20 years 2% 2% (average age) Face FRVT[2002] Varied lighting 10% 1% Outdoor/indoor Voice NIST[2000] Text 10-20% 2-5% Independent
- 7. Multimodal biometric systems • Use of multiple biometric indicators for identifying individuals, known as multimodal biometrics. Combining the evidence obtained from different modalities using an effective fusion scheme can significantly improve the overall accuracy of the biometric system. • . A multimodal biometric system can reduce the FTE/FTC rates and provide more resistance against spoofing because it is difficult to simultaneously spoof multiple biometric sources. • Four levels of information fusion are possible in a multimodal biometric system. They are fusion at the sensor level, feature extraction level, matching score level and decision level.
- 8. . Which Biometric Modalities to Fuse? Voice, Face Voice, Lip Movement Voice, Face, Lip Movement Fingerprint, Face Fingerprint, Face, Voice Fingerprint, Face, Hand geometry Fingerprint, Voice, Hand geometry Fingerprint, Hand geometry Facial thermogram, Face Iris, Face Palmprint, Hand geometry Ear, Voice
- 9. Classification • Multimodal biometric systems that have been proposed can be classified based on four parameters, namely (1) architecture (2) sources that provide multiple evidence (3) level of fusion (4) methodology used for integrating multiple cues
- 10. Architecture • Architecture of a multimodal biometric system refers to the sequence in which the multiple cues are acquired and processed. • Two types 1) serial 2)parallel • Serial Architecture: In the serial or cascade architecture, the processing of the modalities takes place sequentially and the outcome of one modality affects the processing of the subsequent modalities. Ex: bank ATMs • Parallel Architecture :In the parallel design, different modalities operate independently and their results are combined using an appropriate fusion scheme. Ex: in military
- 11. . Serial
- 13. Levels of fusion • Broadly categorized into 2 types a) fusion prior to matching b) fusion after matching • In Fusion prior to matching integration of information can take place either at the sensor level or at the feature level. • Sensor level:: Sensor level fusion can be done only if the multiple cues are either instances of the same biometric trait obtained from multiple compatible sensors or multiple instances of the same biometric trait obtained using a single sensor,ex: 3D model of face. • In sensor level fusion, the multiple cues must be compatible and the correspondences between points in the data must be known in advance. • It may not be possible to integrate face images obtained from cameras with different resolutions
- 14. . • Feature level: When the feature vectors are homogeneous (e.g., multiple fingerprint impressions of a user’s finger), a single resultant feature vector can be calculated as a weighted average of the individual feature vectors. • When the feature vectors are non-homogeneous (e.g., feature vectors of different biometric modalities like face and hand geometry), we can concatenate them to form a single feature vector. features vectors must be compatible. • Integration at the feature level is difficult to achieve in practice because of the following reasons: (i) The relationship between the feature spaces of different biometric systems may not be known. (ii) Concatenating two feature vectors may result in a feature vector with very large dimensionality leading to the ‘curse of dimensionality’ problem.
- 15. • Fusion after matching: categories into 1) Dynamic classifier selection scheme: chooses the results of that classifier which is most likely to give the correct decision for the specific input pattern. 2)Abstract or decision level: can take place when each biometric matcher individually decides on the best match based on the input presented to it. Methods like majority voting,And rule Or rule can be used to arrive at the final decision. 3)Rank level:When the output of each biometric matcher is a subset of possible matches sorted in decreasing order of confidence, the fusion can be done at the rank level.so rank is assigned from highest to lowest level. 4)Matching score level:
- 17. Fusion at the Matching Score Level • Two possible approaches in the context of verification: – Classification approach: A feature vector is constructed using the matching scores. Feature vector is classified as belonging to either genuine or impostor class (e.g., k-Nearest Neighbor, Decision tree) – Combination approach: A single scalar score is generated from multiple matching scores. A classifier is designed to operate on the new score (e.g.simple sum, Min score, max score, matcher weighting,user weighting) Experiments indicate that the combination approach performs better than the classification approach
- 18. Fusion at the Matching Score Level
- 19. Score Normalisation • Scores output by individual matchers: – Non-homogeneous: distance or similarity – Ranges may be different; e.g., [0,100] or [0,1000] – Distributions may be different • To facilitate fusion: – Modify the location and scale parameters of score distributions of individual matchers. – Apply transformation to scores present in the genuine impostor overlap region.
- 20. Normalization Techniques Min-Max(MM):This method maps the raw scores to the [0, 1] range . The quantities max(S) and min(S) specify the end points of the score range: S:set of all scores for that matcher s:a raw matching score Z Score(ZS): Tanh(TH): It maps the raw scores to the (0, 1) range
- 21. • Adaptive(AD): The errors of individual biometric matchers stem from the overlap of the genuine and impostor score distributions. This overlap region represented by its center c and its width w. To decrease the effect of this overlap on the fusion algorithm, an adaptive normalization procedure apply and aims to increase the separation of the genuine and impostor distributions, while still mapping the scores to [0,1] range.
- 22. Biometric Fusion • These are of following types m n i represents the normalized score for matcher m (m=1,2… M ,where M is the number of matchers) applied to user i (i=1,2…I ,where I is the number of individuals in the database). Simple Sum(SS): Min Score(MIS): Max Score(MAS):
- 23. continue • Matcher Weighting(MW): Weights are assigned to the individual matchers based on their Equal Error Rates (EER’s). m Denote the EER of matcher m as e Where m=1,2….M Weight associated with matcher m is calculated as Fused score for user i is calculated as
- 25. Conclusion • Though time taken in Multimodal biometric systems is larger then the Unimodal systems still it is used in place where security is the chief concern. By using appropriate normalization technique and fusion technique we can achieve a high security multimodal biometric system .
- 26. Reference • M. Indovina, U. Uludag, R. Snelick, A. Mink, and A. Jain, “Multimodal Biometric Authentication Methods”, Proc. MMUA 2009, Workshop on Multimodal User Authentication, pp. 99-106, Santa Barbara, CA, Dec. 11-12, 2009. • A. Ross and A.K. Jain, “Information Fusion in Biometrics”, Pattern Recognition Letters, vol. 24, no. 13, pp. 2115-2125, 2003. • R. Auckenthaler, M. Carey, and H. Lloyd-Thomas, “Score Normalization for Text-Independent Speaker Verification Systems”, Digital Signal Processing, vol. 10, pp. 42-54, 2000.