Biometrics: Identity Verification in a Networked World

Transparent Multimodal Biometric System for HD Multimedia Conference

Name: Laith Abbadi, Abbas Javadtalab

Introduction

Identification in electronic networks (e-Identification) is a main topic in e-world (e-commerce and e-government) [1].

A biometric system recognizes a person based on physiological or behavioral characteristics of the person.

A multimodal biometric system is a system that combines two or more of the biometric characteristics.

Purpose

The purpose of the paper is to verify the identity of a person during a video conference using a transparent multimodal biometrics.

The paper will propose a multimodal biometric system using face and ear recognition and without the user’s interaction.

Security Proposed Framework For practical use, the framework is aiming to

satisfy a set of criteria include: (i)Ease of deployment:

▪ A system can be deployed with few additional requirements on current infrastructure and communication protocols;

(ii) Ease of use: ▪ Users have no difficulties using the system;

(iii) Security: ▪ A system should address the real security concerns in

verifying a person in the system. The security may not be perfect, but should be good enough to be user-friendly and business-driven

Authentication Type

Classification Description Example Identifier Group

Type 1 Something you know Password, PIN Knowledge based

Type 2 Something you have Token, OTP Electronic tokens

Type 3 Something you are Fingerprint, iris, face, ear Biometric (Physiological)

Type 4 Something you do Voice, Signature Biometric (Behavioral)

Table 1: Classification of authentication type

Identifier Groups Knowledge Based Identifiers

Date of Birth SIN Number Names Address PIN Numbers (passwords)

Electronic Tokens Digital tokens are the physical devices that contain digital

information for verification purposes. ▪ a. Smart Cards▪ b. One time Password Token

▪ OTP Time based Token▪ OTP Event based Token

▪ c. Radio frequency identifiers (RFID).

Identifier Groups

Biometric Identifiers Physiological Biometric Identifiers:

▪ Fingerprint ▪ Retina image▪ Iris▪ Face Recognition▪ Ear Recognition

Behavioral Biometric Identifiers: ▪ Voice▪ Dynamic signature▪ Keystrokes dynamics

Note: Using behavioral identifiers alone is not a solid solution, but they work well if they used with other types of identifiers.

Biometric System

Features Biometric for Verification

Biometric for Identification

Fingerprint √ √

Iris √

Face Recognition

√ √

Ear Recognition

√

Voice √

Dynamic Signature √

Keystrokes dynamics √

Table 2: Biometric Features

E-identifiers

Table 3: Comparison of e-identifier groups

Source: [1]

Biometric identifiers

Ear

Table 4: Biometric Identifiers [2]

Note: For Ear, research was not finished by Nov 3, 2010

HD Multimedia Conference High quality video Conferencing Used for communicating between CEO’s and

VIP’s Video Quality:1920x1080 x264:

Open source implementation of H.264 standard x264 offers faster encoding

Sample Results (CBR)

Original 1200 Kbit/s

100 Kbit/s800 Kbit/s

Multimodal Biometric Recognition system

Face Templates Face

Matching

Face Extraction

Ear Templates

Fusion

Ear Matching

Ear Extraction

Decision

Conference solution

Conference solution

Future Work

1-  Apply face detection Algorithm 2- Apply ear recognition Algorithm 3- Apply voice detection

a.    Oral style (such as spelling ‘aahhh’) 4- Mouth movement 5- Face Expression & Emotion (ex.

laughing) 8- Ration (face with upper body) 9- Add-ons

a.    Hand shake (haptics)

Conclusion

Use of Multimodal Biometric system is more advantageous than using a mono-modal biometric system

Biometric features are unique to each person

It is feasible to have a transparent verification system using face and ear recognition

Thank You

References [1] Biometric Technology Today (BTT), June (2008) Biometrics in the retail sector page 9-

11 [2] Black Cathryn, (2008) Biometric Technology Today • January 2008page 5 [3] Clarke Roger (2007) Introduction to Information Security, February 2007 [4] Clarke, Roger (2008), ``EDI is but one element of electronic commerce'', Roger

Clarke's EC Foundation Paper http://www.anu.edu.au/people/Roger.Clarke/EC/Bled08.ht [5] Clarke, Roger (2005) Dataveillance by Governments The Technique of Computer

Matching Information Technology & People, Vol. 7 No. 2, 2005, pp. 46-85 [6] Clarke, Roger (2009) Human Identification in Information Systems: Management

Challenges and Public Policy Issues [7] Cranor L, Cytron R. Sensus:a security-conscious electronic polling system for the

Internet. Proceedings of the Hawaii International Conference on System Sciences; 2009. p. 561e70.

[8] Desmarais Norman, (2009) Body language, security and e-commerce Volume 18 . Number 1 . 2009 . pp. 61-74

[9] Granova Anna & Eloff JHP, (2004) Online banking and identity theft: who carries the risk? Computer Fraud and Security page 7-8

[10] Marshalla Angus M., Tompsett Brian, (2005) Identity theft in an online world Computer Law & Security Report (2008) 21, 128e137

[11] Monrose Fabian, Rubin Aviel D. (2009) Keystroke dynamics as a biometric for authentication. Future Generation Computer Systems 16 (2009) 351-359