By Topic

Improved class statistics estimation for sparse data problems in offline signature verification

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Bin Fang ; Dept. of Comput. Sci., Chongqing Univ., China ; Yuan Yan Tang

Sparse data problems are prominent in applications of offline signature verification. By using a small number of training samples, the class statistics estimation errors may be significant, resulting in worsened verification performance. In this paper, we propose two methods to improve the statistics estimation. The first approach employs an elastic distortion model to artificially generate additional training samples for pairs of genuine signatures. These additional samples, together with original genuine samples, are used to compute statistic parameters for a Mahalanobis distance threshold classifier. The other approach is to adopt regularization techniques to overcome the problem of inverting an ill-conditioned sample covariance matrix due to insufficient training samples. A ridge-like estimator is modeled to add some constant values for diagonal elements of the sample covariance matrix. Experimental results showed that both methods were able to improve verification accuracy when they were incorporated with a set of peripheral features. Effectiveness of the methods was validated by quantity analysis.

Published in:

Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on  (Volume:35 ,  Issue: 3 )