Principal Component Analysis for Symmetric Key Generation

This work presents a novel biometric encryption scheme based on feature vectors extracted from a face recognition system. This system uses principal component analysis, in order to generate a symmetric secret key, being this key used to encrypt any information data, like a biometric template. The da...

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Bibliographic Details
Published in:Revista IEEE América Latina 2004-03, Vol.2 (1), p.1-1
Main Authors: Gustavo Lizarraga, M, Lee, Luan Ling
Format: Article
Language:English
Online Access:Get full text
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Summary:This work presents a novel biometric encryption scheme based on feature vectors extracted from a face recognition system. This system uses principal component analysis, in order to generate a symmetric secret key, being this key used to encrypt any information data, like a biometric template. The data is therefore concealed and only an individual having a similar biometric feature vector is capable to regenerate the correct key. This scheme is applied to a system using eigenfaces for recognition, where the corrected detected class from a sample image can guarantee the corrected generation of a symmetric key. Due to the efficiency of the system being dependent of the face recognition algorithm, the tests showed a rate of 90.4% of corrected symmetric key generation, or sucessfull encryption/ decryption scheme, for 25 face classes, with 5 images each.
ISSN:1548-0992
1548-0992