Abstract
With the wonders of the Internet and the promises of the worldwide information infrastructure, a highly secure authentication system is desirable. Biometric has been deployed in this purpose as it is a unique identifier. However, it also suffers from inherent limitations and specific security threats such as biometric fabrication. To alleviate the liabilities of the biometric, a combination of token and biometric for user authentication and verification is introduced. All user data is kept in the token and human can get rid of the task of remembering passwords. The proposed framework is named as Bio-Discretization. Bio-Discretization is performed on the face image features, which is generated from Non-Negative Matrix Factorization (NMF) in the wavelet domain to produce a set of unique compact bitstring by iterated inner product between a set of pseudo random numbers and face images. Bio-Discretization possesses high data capture offset tolerance, with highly correlated bitstring for intraclass data. This approach is highly desirable in a secure environment and it outperforms the classic authentication scheme.
| Original language | English |
|---|---|
| Pages (from-to) | 64-73 |
| Number of pages | 10 |
| Journal | Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) |
| Volume | 3339 |
| Publication status | Published - 2004 Dec 1 |
| Event | 17th Australian Joint Conference on Artificial Intelligence, AI 2004: Advances in Artificial Intelligence - Cairns, Australia Duration: 2004 Dec 4 → 2004 Dec 6 |
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All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
Cite this
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Bio-discretization : Biometrics authentication featuring face data and tokenised random number. / Foon, Neo Han; Teoh, Beng Jin; Ling, David Ngo Chek.
In: Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), Vol. 3339, 01.12.2004, p. 64-73.Research output: Contribution to journal › Conference article
TY - JOUR
T1 - Bio-discretization
T2 - Biometrics authentication featuring face data and tokenised random number
AU - Foon, Neo Han
AU - Teoh, Beng Jin
AU - Ling, David Ngo Chek
PY - 2004/12/1
Y1 - 2004/12/1
N2 - With the wonders of the Internet and the promises of the worldwide information infrastructure, a highly secure authentication system is desirable. Biometric has been deployed in this purpose as it is a unique identifier. However, it also suffers from inherent limitations and specific security threats such as biometric fabrication. To alleviate the liabilities of the biometric, a combination of token and biometric for user authentication and verification is introduced. All user data is kept in the token and human can get rid of the task of remembering passwords. The proposed framework is named as Bio-Discretization. Bio-Discretization is performed on the face image features, which is generated from Non-Negative Matrix Factorization (NMF) in the wavelet domain to produce a set of unique compact bitstring by iterated inner product between a set of pseudo random numbers and face images. Bio-Discretization possesses high data capture offset tolerance, with highly correlated bitstring for intraclass data. This approach is highly desirable in a secure environment and it outperforms the classic authentication scheme.
AB - With the wonders of the Internet and the promises of the worldwide information infrastructure, a highly secure authentication system is desirable. Biometric has been deployed in this purpose as it is a unique identifier. However, it also suffers from inherent limitations and specific security threats such as biometric fabrication. To alleviate the liabilities of the biometric, a combination of token and biometric for user authentication and verification is introduced. All user data is kept in the token and human can get rid of the task of remembering passwords. The proposed framework is named as Bio-Discretization. Bio-Discretization is performed on the face image features, which is generated from Non-Negative Matrix Factorization (NMF) in the wavelet domain to produce a set of unique compact bitstring by iterated inner product between a set of pseudo random numbers and face images. Bio-Discretization possesses high data capture offset tolerance, with highly correlated bitstring for intraclass data. This approach is highly desirable in a secure environment and it outperforms the classic authentication scheme.
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M3 - Conference article
VL - 3339
SP - 64
EP - 73
JO - Lecture Notes in Computer Science
JF - Lecture Notes in Computer Science
SN - 0302-9743
ER -