Handwritten Signature Verification Using Image Invariants and Dynamic Features

In this paper, a development of automatic signature classification system is proposed. We have presented offline and online signature verification system, based on the signature invariants and its dynamic features. The proposed system segments each signature based on its perceptually important points and then, for each segment, computes a number of features that are scale, rotation and displacement invariant. The normalized moments and the normalized Fourier descriptors are used for this invariancy, while the speed of pen is used as a dynamic feature of the signature. In both cases the data acquisition, pre-processing, feature extraction and comparison steps are analyzed and discussed. Both static and dynamic features were used as an input to a neural network. The neural network used for classification is a multi-layer perceptron (MLP) with one input layer, one hidden layer and one output layer. The performance of the proposed system is presented through simulation examples

[1]  Réjean Plamondon,et al.  Segmenting Handwritten Signatures at Their Perceptually Important Points , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[3]  Abhijit Mahalanobis,et al.  Unified framework for the synthesis of synthetic discriminant functions with reduced noise variance and sharp correlation structure , 1990 .

[4]  Sargur N. Srihari,et al.  On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Roland Wilson,et al.  A Neural Network for Triad Classification , 1995, International Conference on Mathematics and Computing.

[6]  P. Wintz,et al.  An efficient three-dimensional aircraft recognition algorithm using normalized fourier descriptors , 1980 .

[7]  M. Furst,et al.  Neural network based model for classification of music type , 1995, Eighteenth Convention of Electrical and Electronics Engineers in Israel.

[8]  Toby Berger,et al.  Reliable On-Line Human Signature Verification Systems , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Jianying Hu,et al.  A Hidden Markov Model approach to online handwritten signature verification , 1998, International Journal on Document Analysis and Recognition.

[10]  Jonas Richiardi,et al.  On-line signature verification resilience to packet loss in IP networks , 2004 .

[11]  Pavel Pudil,et al.  Introduction to Statistical Pattern Recognition , 2006 .

[12]  K. Raghunath Rao,et al.  On the recognition of occluded shapes and generic faces using multiple-template expansion matching , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Berrin Yanikoglu,et al.  An improved decision criterion for genuine / forgery classification in on-line signature verification , 2003 .

[14]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

[15]  Yann LeCun,et al.  Signature Verification Using A "Siamese" Time Delay Neural Network , 1993, Int. J. Pattern Recognit. Artif. Intell..

[16]  Anil K. Jain,et al.  Biometric technology for human identification , 2004 .

[17]  Jianying Hu,et al.  Retail applications of signature verification , 2004, SPIE Defense + Commercial Sensing.

[18]  D Casasent,et al.  Advanced distortion-invariant minimum average correlation energy (MACE) filters. , 1992, Applied optics.

[19]  Sargur N. Srihari,et al.  Learning strategies and classification methods for off-line signature verification , 2004, Ninth International Workshop on Frontiers in Handwriting Recognition.

[20]  L. Yang,et al.  Application of hidden Markov models for signature verification , 1995, Pattern Recognit..