Pattern Spectrum and Multiscale Shape Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatial Size Distributions: Applications to Shape and Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A granulometric analysis of specular microscopy images of human corneal endothelia
Computer Vision and Image Understanding
Off-line signature verification using DTW
Pattern Recognition Letters
Offline signature verification using the discrete radon transform and a hidden Markov model
EURASIP Journal on Applied Signal Processing
Signature Verification Using a Bayesian Approach
IWCF '08 Proceedings of the 2nd international workshop on Computational Forensics
Stroke-Morphology Analysis Using Super-Imposed Writing Movements
IWCF '08 Proceedings of the 2nd international workshop on Computational Forensics
Signature verification (SV) toolbox: Application of PSO-NN
Engineering Applications of Artificial Intelligence
A Machine Learning Approach to Off-Line Signature Verification Using Bayesian Inference
IWCF '09 Proceedings of the 3rd International Workshop on Computational Forensics
A granulometric analysis of specular microscopy images of human corneal endothelia
Computer Vision and Image Understanding
Off-line signature recognition using morphological pixel variance analysis
Proceedings of the International Conference and Workshop on Emerging Trends in Technology
Off-line signature verification and forgery detection using fuzzy modeling
Pattern Recognition
Similarity computation based on feature extraction for off-line Chinese signature verification
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Modeling the pattern spectrum as a Markov process and its use for efficient shape classification
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A hybrid ANN-based technique for signature verification
CI'10 Proceedings of the 4th WSEAS international conference on Computational intelligence
A writer-independent off-line signature verification system based on signature morphology
Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia
Computationally efficient, one-pass algorithm for morphological filters
Journal of Visual Communication and Image Representation
Offline signature verification and identification by hybrid features and Support Vector Machine
International Journal of Artificial Intelligence and Soft Computing
A multi-objective memetic algorithm for intelligent feature extraction
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Similarity measurement for off-line signature verification
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
A study on enhanced dynamic signature verification for the embedded system
BVAI'05 Proceedings of the First international conference on Brain, Vision, and Artificial Intelligence
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A fundamental problem in the field of off-line signature verification is the lack of a signature representation based on shape descriptors and pertinent features. The main difficulty lies in the local variability of the writing trace of the signature which is closely related to the identity of human beings. In this paper, we propose a new formalism for signature representation based on visual perception. A signature image consists of 512 脳 128 pixels and is centered on a grid of rectangular retinas which are excited by local portions of the signature. Granulometric size distributions are used for the definition of local shape descriptors in an attempt to characterize the amount of signal activity exciting each retina on the focus of the attention grid. Experimental evaluation of this scheme is made using a signature database of 800 genuine signatures from 20 individuals. Two types of classifiers, a Nearest Neighbor and a threshold classifier, show a total error rate below 0.02 percent and 1.0 percent, respectively, in the context of random forgeries.