Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biometric Recognition: Security and Privacy Concerns
IEEE Security and Privacy
Accelerating the SVD Block-Jacobi Method
Computing - Editorial: Special issue on GAMM – Workshop on Guaranteed Error-bounds for the Solution of Nonlinear Problems in Applied Mathematics
On-line signature recognition based on VQ-DTW
Pattern Recognition
Journal of Cognitive Neuroscience
A novel approach for Online signature verification using fisher based probabilistic neural network
ISCC '10 Proceedings of the The IEEE symposium on Computers and Communications
Fast on-line signature recognition based on VQ with time modeling
Engineering Applications of Artificial Intelligence
The 4NSigComp2010 Off-line Signature Verification Competition: Scenario 2
ICFHR '10 Proceedings of the 2010 12th International Conference on Frontiers in Handwriting Recognition
Efficient on-line signature recognition based on multi-section vector quantization
Pattern Analysis & Applications
Signature authentication based on subpattern analysis
Applied Soft Computing
Rough set approach to online signature identification
Digital Signal Processing
An introduction to biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
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Biometric technologies are the primary tools for certifying identity of individuals. But cost of sensing hardware plus degree of physical invasion required to obtain reasonable success are considered major drawbacks. Nevertheless, the signature is generally accepted as one means of identification. We present an approach on signature recognition using face recognition algorithms to obtain class descriptors and then use a simple classifier to recognize signatures. We also present an algorithm to store the writing direction of a signature, applying a linear transformation to encode this data as a gray scale pattern into the image. The signatures are processed applying Principal Components Analysis and Linear Discriminant Analysis creating descriptors that can be identified using a KNN classifier. Results revealed an accuracy performance rate of 97.47% under cross-validation over binary images and an improvement of 98.60% of accuracy by encoding simulated dynamic parameters. The encoding of real dynamic data boosted the performance rate from 90.21% to 94.70% showing that this technique can be a serious contender to other signature recognition methods.