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...

Full description

Saved in:
Bibliographic Details
Published in:Revista IEEE América Latina 2004-03, Vol.2 (1), p.63-68
Main Authors: Fleury Medeiros, G.C., Gustavo Lizarraga, M., Luan Ling Lee
Format: Article
Language:English
Subjects:
authentication
Biometrics
cryptography
eigenfaces
Electronic mail
Encryption
Face detection
Face recognition
feature extraction
Internet
Laser radar
Mathematical analysis
Principal component analysis
Recognition
Sockets
Testing
Vectors (mathematics)
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
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
DOI:10.1109/TLA.2004.1468644