Robot vision
Classification of Partial 2-D Shapes Using Fourier Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Off-Line Signature Verification by Local Granulometric Size Distributions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual Identification by Signature Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local correspondence for detecting random forgeries
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Analysis of Handwriting Individuality Using Word Features
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Learning Strategies and Classification Methods for Off-Line Signature Verification
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Signature Verification Using a Bayesian Approach
IWCF '08 Proceedings of the 2nd international workshop on Computational Forensics
ICDAR 2009 Signature Verification Competition
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
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A machine learning approach to off-line signature verification is presented. The prior distributions are determined from genuine and forged signatures of several individuals. The task of signature verification is a problem of determining genuine-class membership of a questioned (test) signature. We take a 3-step, writer independent approach: 1) Determine the prior parameter distributions for means of both "genuine vs. genuine" and "forgery vs. known" classes using a distance metric. 2) Enroll n genuine and m forgery signatures for a particular writer and calculate both the posterior class probabilities for both classes. 3) When evaluating a questioned signature, determine the probabilities for each class and choose the class with bigger probability. By using this approach, performance over other approaches to the same problem is dramatically improved, especially when the number of available signatures for enrollment is small. On the NISDCC dataset, when enrolling 4 genuine signatures, the new method yielded a 12.1% average error rate, a significant improvement over a previously described Bayesian method.