Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Wavelet-based off-line handwritten signature verification
Computer Vision and Image Understanding
Introduction to Computer Graphics
Introduction to Computer Graphics
Off-line Signature Verification Using HMM for Random, Simple and Skilled Forgeries
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Object classification and recognition using Toeplitz matrices
Artificial intelligence and security in computing systems
Dynamics features Extraction for on-Line Signature verification
CONIELECOMP '04 Proceedings of the 14th International Conference on Electronics, Communications and Computers
ER2: An Intuitive Similarity Measure for On-Line Signature Verification
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Handwritten Signature Verification Using Image Invariants and Dynamic Features
CGIV '06 Proceedings of the International Conference on Computer Graphics, Imaging and Visualisation
The Compact Three Stages Method of the Signature Recognition
CISIM '07 Proceedings of the 6th International Conference on Computer Information Systems and Industrial Management Applications
Offline signature verification using the discrete radon transform and a hidden Markov model
EURASIP Journal on Applied Signal Processing
Glove-Based Approach to Online Signature Verification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Determining the similarity of signatures on the basis of characteristic points analysis
International Journal of Biometrics
Dynamic signature recognition based on modified windows technique
CISIM'12 Proceedings of the 11th IFIP TC 8 international conference on Computer Information Systems and Industrial Management
Hi-index | 0.00 |
Dynamic signature analysis allows us to register individuals and their hidden human behaviour. This paper presents a stroke-based approach to dynamic analysis of signature. Individual features can be identified by finding the discrete signature points like x,y-coordinates, pressure, time and pen velocity. Between signatures, the correlation measure is determined. The dynamic features are extracted from authentic and forged signatures. Experimental results show that measurement of dynamic features (velocity changes) contains important information and offers a high level of accuracy for signature verification in comparison with the results without such measurements, which will be explained in the following parts of the paper.