Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wavelet-based off-line handwritten signature verification
Computer Vision and Image Understanding
Machine Learning
Off-Line Signature Verification: Recent Advances and Perspectives
BSDIA '97 Proceedings of the First Brazilian Symposium on Advances in Document Image Analysis
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
Offline Geometric Parameters for Automatic Signature Verification Using Fixed-Point Arithmetic
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Off-line Signature Verification Method based on Graph
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Offline signature verification using the discrete radon transform and a hidden Markov model
EURASIP Journal on Applied Signal Processing
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Signature verification is a common task in forensic document analysis. It is one of determining whether a questioned signature matches known signature samples. From the viewpoint of automating the task it can be viewed as one that involves machine learning from a population of signatures. There are two types of learning to be accomplished. In the first, the training set consists of genuines and forgeries from a general population. In the second there are genuine signatures in a given case. The two learning tasks are called person-independent (or general) learning and person-dependent (or special) learning. General learning is from a population of genuine and forged signatures of several individuals, where the differences between genuines and forgeries across all individuals are learnt. The general learning model allows a questioned signature to be compared to a single genuine signature. In special learning, a person's signature is learnt from multiple samples of only that person's signature– where within-person similarities are learnt. When a sufficient number of samples are available, special learning performs better than general learning (5% higher accuracy). With special learning, verification accuracy increases with the number of samples.