Random Multispace Quantization as an Analytic Mechanism for BioHashing of Biometric and Random Identity Inputs

• Published in: IEEE transactions on pattern analysis and machine intelligence 2006-12, Vol.28 (12), p.1892-1901
• Main Authors: Teoh, A.B.J., Goh, A., Ngo, D.C.L.
• Format: Article
• Language: English
• Subjects: Applied sciences; Artificial Intelligence; BioHashing; Bioinformatics; Biometrics; Biometry - methods; Cancellable biometrics; Computer science; control theory; systems; Computer Security; Computer systems and distributed systems. User interface; Cryptography; Data Compression - methods; Data Interpretation, Statistical; Database Management Systems; Databases, Factual; Exact sciences and technology; Face - anatomy & histology; face recognition; Fingerprint recognition; Humans; Image Interpretation, Computer-Assisted - methods; Intelligence; Iris; Mathematical analysis; Memory and file management (including protection and security); Memory organisation. Data processing; Passwords; Pattern analysis; Pattern Recognition, Automated - methods; Pattern recognition. Digital image processing. Computational geometry; Polynomials; Protection; Quantization; random multispace quantization; Recognition; Security; Software; Testing
• ISSN: 0162-8828; 1939-3539; 2160-9292
• DOI: 10.1109/TPAMI.2006.250

Biometric analysis for identity verification is becoming a widespread reality. Such implementations necessitate large-scale capture and storage of biometric data, which raises serious issues in terms of data privacy and (if such data is compromised) identity theft. These problems stem from the essential permanence of biometric data, which (unlike secret passwords or physical tokens) cannot be refreshed or reissued if compromised. Our previously presented biometric-hash framework prescribes the integration of external (password or token-derived) randomness with user-specific biometrics, resulting in bitstring outputs with security characteristics (i.e., noninvertibility) comparable to cryptographic ciphers or hashes. The resultant BioHashes are hence cancellable, i.e., straightforwardly revoked and reissued (via refreshed password or reissued token) if compromised. BioHashing furthermore enhances recognition effectiveness, which is explained in this paper as arising from the random multispace quantization (RMQ) of biometric and external random inputs