Offline Geometric Parameters for Automatic Signature Verification Using Fixed-Point Arithmetic
IEEE Transactions on Pattern Analysis and Machine Intelligence
A comparison of SVM and HMM classifiers in the off-line signature verification
Pattern Recognition Letters
Fuzzy shape-memory snakes for the automatic off-line signature verification problem
Fuzzy Sets and Systems
Snake models for offline signature verification: a comparative study
International Journal of Innovative Computing and Applications
Off-line signature recognition using morphological pixel variance analysis
Proceedings of the International Conference and Workshop on Emerging Trends in Technology
Offline signature verification based on discrete cosine transform
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
Offline signature verification based on statistical features
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
Learning Vector Quantisation based recognition of offline handwritten signatures
International Journal of Biometrics
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The off-line signature verification rests on the hypothesis that each writer has similarity amongsignature samples, with small distortion and scale variability. This kind of distortion represents the intrapersonal variability [3]. This paper reports the interpersonal and intrapersonal variability influences in a software approach based on Hidden Markov Model (HMM) classifier [1,5,7]. The experiments have shown the error rates variability considering different forgery types, random, simples and skilled forgeries. The mathematical approach and the resulting software also report considerations in a real application problem.