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..
Continue(2) Non-universality: If every individual in the targetpopulation is able to present the biometric trait for recognition,then the trait is said to be universal. However, not all biometrictraits are truly universal. people with hand-related disabilities, manual workers withmany cuts and bruises on their fingertips, and people with veryoily or dry fingers NIST reported 2% people cannot enroll using finger print.
Continue(3) Lack of individuality : Features extracted frombiometric characteristics of different individuals can be quitesimilar. A small proportion of the population can have nearly identicalfacial appearance due to genetic factors (e.g., father and son,identical twins, etc.)(4) Lack of invariant representation :The biometricdata acquired from a user during verification will not be identicalto the data used for generating the user’s template duringenrollment.
Continue(5) Susceptibility to circumvention:Although it isvery difficult to steal someone’s biometric traits, it is stillpossible for an impostor to circumvent a biometric system usingspoofed traits. Behavioral traits like signature and voice are more susceptibleto such attacks than physiological traits. TEST TEST FALSE FALSE PARAMETER REJECT RATE ACCEPT RATEFingerprint FVC 20 years 2% 2% (average age)Face FRVT Varied lighting 10% 1% Outdoor/indoorVoice NIST Text 10-20% 2-5% Independent
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.
.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
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
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
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
.• 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.
• 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:
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 isgenerated from multiple matching scores. A classifieris designed to operate on the new score (e.g.simple sum,Min score, max score, matcher weighting,user weighting) Experiments indicate that the combination approachperforms better than the classification approach
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.
Normalization TechniquesMin-Max(MM):This method maps the raw scores to the [0, 1]range . The quantities max(S) and min(S) specify the end points ofthe score range:S:set of all scores for that matchers:a raw matching scoreZ Score(ZS):Tanh(TH):It maps the raw scores to the (0, 1) range
• 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.
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 useri (i=1,2…I ,where I is the number of individuals in the database).Simple Sum(SS):Min Score(MIS):Max Score(MAS):
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….MWeight associated with matcher m is calculated asFused score for user i is calculated as
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 .
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.