Random translational transformation for changeable face verification
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Sorted index numbers for privacy preserving face recognition
EURASIP Journal on Advances in Signal Processing - Special issue on recent advances in biometric systems: a signal processing perspective
Biometrics-based identifiers for digital identity management
Proceedings of the 9th Symposium on Identity and Trust on the Internet
A hybrid approach for generating secure and discriminating face template
IEEE Transactions on Information Forensics and Security
A simple and efficient eigenfaces method
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
VisualSec: a secure message delivery scheme for online social networks based on profile images
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Perceptual image hashing based on virtual watermark detection
IEEE Transactions on Image Processing
An analysis of random projection for changeable and privacy-preserving biometric verification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part II
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Biometric hashing based on genetic selection and its application to on-line signatures
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Two-factor face authentication using matrix permutation transformation and a user password
Information Sciences: an International Journal
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In this paper, we describe a biometric hash algorithm for robust extraction of bits from face images. While a face-recognition system has high acceptability, its accuracy is low. The problem arises because of insufficient capability of representing features and variations in data. Thus, we use dimensionality reduction to improve the capability to represent features, error correction to improve robustness with respect to within-class variations, and random projection and orthogonalization to improve discrimination among classes. Specifically, we describe several dimensionality-reduction techniques with biometric hashing enhancement for various numbers of bits extracted. The theoretical results are evaluated on the FERET face database showing that the enhanced methods significantly outperform the corresponding raw methods when the number of extracted bits reaches 100. The improvements of the postprocessing stage for principal component analysis (PCA), Wavelet Transform with PCA, Fisher linear discriminant, Wavelet Transform, and Wavelet Transform with Fourier-Mellin Transform are 98.02%, 95.83%, 99.46%, 99.16%, and 100%, respectively. The proposed technique is quite general, and can be applied to other biometric templates. We anticipate that this algorithm will find applications in cryptographically secure biometric authentication schemes.