Offline Signature Verification Using Real Adaboost Classifier Combination of Pseudo-dynamic Features

  title={Offline Signature Verification Using Real Adaboost Classifier Combination of Pseudo-dynamic Features},
  author={Juan Hu and Youbin Chen},
  journal={2013 12th International Conference on Document Analysis and Recognition},
We present an offline signature verification system using three different pseudo-dynamic features, two different classifier training approaches and two datasets. One of the most difficult problems of off-line signature verification is that the signature is just a static image while losing a lot of useful dynamic information. Three separate pseudo-dynamic features based on gray level: local binary pattern (LBP), gray level co-occurrence matrix (GLCM) and histogram of oriented gradients (HOG) are… CONTINUE READING
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Key Quantitative Results

  • The fusion of all features achieves the best result of 7.66% and 9.94% equal error rate in GPDS while 7.55% and 11.55% equal error rate in CSD respectively.


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Fusion of features and classifiers for off-line handwritten signature verification

The First Asian Conference on Pattern Recognition • 2011
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Alonso.Signature Verification using Local Directional Pattern (LDP)

Miguel A.Ferrer, Francisco Vargas, Carlos M.Travieso, B Jesus
IEEE International Carnahan Conference on Security Technology (ICCST), • 2010
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