A New Decision Making Approach for Improving the Performance of Automatic Signature Verification Using Multi-sets of Features

So far, Automatic Signature Verification (ASV) approaches using a threshold-based decision have depended on one feature set for distance measure and a threshold on this distance measure for verification. The best performance that can be reached in this case is the one obtained by using the best feature set (bfs). In this paper, we introduce a new decision making approach for ASV that uses Multi-Sets of Features (MSF). The MSF provides higher performance than that obtainable by using the bfs, with better forgery detection. The improvement is seen to be significant because it recovers some lost effectiveness and can add it to that of the bfs. This gain in effectiveness is highly desirable when we deal with signatures of high value documents.

[1]  Vallipuram Muthukkumarasamy,et al.  Off-line Signature Verification Using Enhanced Modified Direction Features in Conjunction with Neural Classifiers and Support Vector Machines , 2007 .

[2]  Réjean Plamondon,et al.  Automatic Signature Verification: The State of the Art - 1989-1993 , 1994, Int. J. Pattern Recognit. Artif. Intell..

[3]  Enrique Frías-Martínez,et al.  Support vector machines versus multi-layer perceptrons for efficient off-line signature recognition , 2006, Eng. Appl. Artif. Intell..

[4]  Réjean Plamondon,et al.  Automatic signature verification and writer identification - the state of the art , 1989, Pattern Recognit..

[5]  Robert Sabourin,et al.  On the implementation of some graphometric techniques for interactive signature verification : a feasability study , 1987 .

[6]  A. N. Rajagopalan,et al.  Off-line signature verification using DTW , 2007, Pattern Recognit. Lett..

[7]  Miguel A. Ferrer,et al.  Off-line Signature Verification Based on High Pressure Polar Distribution , 2008 .

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

[9]  Teruo Fukumura,et al.  FEATURE EXTRACTION AND SELECTION FOR SIMULATED SIGNATURE VERIFICATION , 1989 .

[10]  M. Ammar.  Performance of parametric and reference pattern based features in static signature verification: a comparative study , 1990, [1990] Proceedings. 10th International Conference on Pattern Recognition.

[11]  Cemal Ardil,et al.  Automatic Authentication of Handwritten Documents via Low Density Pixel Measurements , 2008 .

[12]  Teruo Fukumura,et al.  Off-Line Preprocessing and Verification of Signatures , 1988, Int. J. Pattern Recognit. Artif. Intell..

[13]  Maan Ammar,et al.  Progress in Verification of Skillfully Simulated Handwritten Signatures , 1991, Int. J. Pattern Recognit. Artif. Intell..

[14]  Hong Yan,et al.  Off-line signature verification based on geometric feature extraction and neural network classification , 1997, Pattern Recognit..

[15]  Mario Vento,et al.  Signature Verification: Increasing Performance by a Multi-Stage System , 2000, Pattern Analysis & Applications.

[16]  Jesus Francisco Vargas Bonilla,et al.  Offline Signature Verification Based on Pseudo-Cepstral Coefficients , 2009, 2009 10th International Conference on Document Analysis and Recognition.