Bio-discretization: Biometrics authentication featuring face data and tokenised random number

Neo Han Foon, Andrew Teoh Beng Jin, David Ngo Chek Ling

Research output: Contribution to journalConference articlepeer-review


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 languageEnglish
Pages (from-to)64-73
Number of pages10
JournalLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Publication statusPublished - 2004
Event17th Australian Joint Conference on Artificial Intelligence, AI 2004: Advances in Artificial Intelligence - Cairns, Australia
Duration: 2004 Dec 42004 Dec 6

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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