Signature recognition through spectral analysis
Pattern Recognition
Elements of information theory
Elements of information theory
CCS '99 Proceedings of the 6th ACM conference on Computer and communications security
Wavelet-based off-line handwritten signature verification
Computer Vision and Image Understanding
Wavelet-Based Compared to Function-Based On-Line Signature Verification
SIBGRAPI '02 Proceedings of the 15th Brazilian Symposium on Computer Graphics and Image Processing
Biometric Hash based on Statistical Features of Online Signatures
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
On-line Handwritten Signature Verification Using Wavelets and Back-propagation Neural Networks
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Online signature verification using a new extreme points warping technique
Pattern Recognition Letters
Secure smartcardbased fingerprint authentication
WBMA '03 Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications
AUTOID '05 Proceedings of the Fourth IEEE Workshop on Automatic Identification Advanced Technologies
IEEE Transactions on Knowledge and Data Engineering
Handwriting: feature correlation analysis for biometric hashes
EURASIP Journal on Applied Signal Processing
Generation of replaceable cryptographic keys from dynamic handwritten signatures
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
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We introduce a novel method for secure computation of biometric hash on dynamic hand signatures using BioPhasor mixing and 2N discretization. The use of BioPhasor as the mixing process provides a one-way transformation that precludes exact recovery of the biometric vector from compromised hashes and stolen tokens. In addition, our user-specific 2N discretization acts both as an error correction step as well as a real-to-binary space converter. We also propose a new method of extracting compressed representation of dynamic hand signatures using discrete wavelet transform (DWT) and discrete fourier transform (DFT). Without the conventional use of dynamic time warping, the proposed method avoids storage of user's hand signature template. This is an important consideration for protecting the privacy of the biometric owner. Our results show that the proposed method could produce stable and distinguishable bit strings with equal error rates (EERs) of 0% and 9.4% for random and skilled forgeries for stolen token (worst case) scenario, and 0% for both forgeries in the genuine token (optimal) scenario.