Paper multi-modal biometric system using fingerprint , face and speech

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A Multi-modal Biometric System Using Fingerprint , Face and SpeechBy Alaa Mohammed Khattab5/16/20151

ContentIntroduction.Multimodal Biometric System.Verification module.Fingerprint verificationFace recognitionSpeaker verificationDecision fusion.Performance EvaluationDatabasesBenchmarksConclusions5/16/20152

IntroductionBiometric 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.5/16/20153

Introduction

5/16/20154This system take the advantage of the capabilities of each individual biometrics

IntroductionSystem consist of four components:Acquisition moduleTemplate databaseEnrollment moduleVerification module5/16/20155

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Acquisition module

Enrollment moduleVerification moduleTemplateDatabase


Formulation5/16/20158fingerprintfacespeechUser identity

Formulation5/16/20159


Multimodal Biometric System

The verification process consists of four stages:Fingerprint verificationFace recognitionSpeaker verificationDecision fusion5/16/201511

Fingerprint Verification5/16/201512

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Fingerprint VerificationFingerprint 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 endingBifurcation

Fingerprint VerificationSteps:Minutiae extracting : extract minutiae from input finger print images.

Minutiae matching : determine the similarity of two minutiae patterns.5/16/201514

Fingerprint Verification5/16/201515

Face Recognition

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Face RecognitionThere 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.5/16/201517

Face RecognitionIn our system : eigenface approach is used.The eigenface-based face recognition method is divided into two stages:Training stage.Operational stage.5/16/201518

Face RecognitionTraining 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.5/16/201519

Face Recognition5/16/201520

Speaker Verification5/16/201521

Speaker VerificationText-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.5/16/201522

Speaker Verification5/16/201523

Decision Fusion5/16/201524




DatabaseA training database of 50 users was collected.

For each user : 10 fingerprint images using optical fingerprint scanner.

9 face images using Panasonic video camera.

12 speech samples using Laptec microphone.5/16/201528

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.5/16/201529

Benchmark (ROC)5/16/201530

false acceptance rateauthenticate acceptance rate


ConclusionMultimodal 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.5/16/201532

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Paper multi-modal biometric system using fingerprint , face and speech

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