Using Dynamic Programming for Solving Variational Problems in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast, simple active contour algorithm for biomedical images
Pattern Recognition Letters
Signature identification through the use of deformable structures
Signal Processing - Special issue on deformable models and techniques for image and signal processing
Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
An off-line signature verification system using an extracted displacement function
Pattern Recognition Letters
Genetic Snakes for Medical Images Segmentation
EvoIASP '99/EuroEcTel '99 Proceedings of the First European Workshops on Evolutionary Image Analysis, Signal Processing and Telecommunications
SIBGRAPI '02 Proceedings of the 15th Brazilian Symposium on Computer Graphics and Image Processing
A Fuzzy Perception for Off-Line Handwritten Signature Verification
BSDIA '97 Proceedings of the First Brazilian Symposium on Advances in Document Image Analysis
Static Signature Verification Employing a Kosko-Neuro-fuzzy Approach
AFSS '02 Proceedings of the 2002 AFSS International Conference on Fuzzy Systems. Calcutta: Advances in Soft Computing
A comparison of SVM and HMM classifiers in the off-line signature verification
Pattern Recognition Letters
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
Off-line signature verification using DTW
Pattern Recognition Letters
Off-line signature verification and forgery detection using fuzzy modeling
Pattern Recognition
Force field analysis snake: an improved parametric active contour model
Pattern Recognition Letters
Shape analysis and fuzzy control for 3d competitive segmentation of brain structures with level sets
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Online Handwritten Character Recognition for a Personal Computer System
IEEE Transactions on Consumer Electronics
Comparing elastic alignment algorithms for the off-line signature verification problem
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
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This paper introduces an adapted fuzzy snake approach for efficiently solving some of the practical constraints in the off-line signature verification problem. Our method is called fuzzy shape-memory snakes due to its resemblance to shape-memory alloys, which are metals that in high-temperature conditions can remember their original shape. In our approach, the snake also ''remembers'' its geometry during its iterative adjustment to a test signature. Off-line signature verification aims to establish the degree of genuineness of a given test signature when compared to a reference signature. Due to the shape and size variability in signatures of the same subject, a system with tolerance to imprecision and also with some ''memory'' of its initial configured shape, would be very useful for this complex verification problem. To our knowledge, snakes and other active contour models have not been previosly applied to the offline signature verification problem. We consider that they could be properly adapted to be useful for this task. Consequently, we have developed a fuzzy snake framework for signature verification which takes into account some practical constraints of this problem when applied to bank checks. Over other signature verification systems, our approach has the advantage of using only one training signature per person. We introduce the fuzziness for the considered signature verification problem in a double direction. First, when iteratively adjusting a shape-memory snake (which is obtained from the training signature) to a considered test signature. Second, when measuring the similarity degree between the snake and the test signature after the adjustment (or verification task) using a Takagi-Sugeno fuzzy inference system, which is trained with three signature features (coincidence, distance and energy) provided by the adjustment. Some advantages of our approach are that: some involved parameters in the internal (shape) snake energy are now eliminated, and a more efficient and natural snake adjustment to the test signature is achieved. This paper also provides a study of the biometric classification errors when comparing our off-line signature verification approach to other non-fuzzy ones using the same signature database.