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Biometrics: Identity Verification in a Networked World
Transparent Multimodal Biometric System for HD Multimedia Conference
Name: Laith Abbadi, Abbas Javadtalab
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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.
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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.
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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
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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
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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).
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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.
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Biometric System
Features Biometric for Verification
Biometric for Identification
Fingerprint √ √
Iris √
Face Recognition
√ √
Ear Recognition
√
Voice √
Dynamic Signature √
Keystrokes dynamics √
Table 2: Biometric Features
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E-identifiers
Table 3: Comparison of e-identifier groups
Source: [1]
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Biometric identifiers
Ear
Table 4: Biometric Identifiers [2]
Note: For Ear, research was not finished by Nov 3, 2010
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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
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Sample Results (CBR)
Original 1200 Kbit/s
100 Kbit/s800 Kbit/s
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Multimodal Biometric Recognition system
Face Templates
Face Matching
Face Extraction
Ear Templates
Fusion
Ear Matching
Ear Extraction
Decision
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Conference solution
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Conference solution
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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)
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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
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Thank You
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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