Choquet fuzzy integral based verification of handwritten signatures

For dealing with adjacent input fuzzy sets having overlapping information, non-additive fuzzy rules are formulated by defining their consequent as a function of fuzzy measures, i.e., a simple form of Choquet integral. The fuzzy measures aggregate the information from the overlapping fuzzy sets using the λ-measure. The defuzzified output of these rules is also in the general form of the Choquet fuzzy integral. The underlying non-additive fuzzy model is investigated for both identification and control of non-linear systems. The identification of this fuzzy model involves the strength of the rules as the known input functions and fuzzy densities required to compute fuzzy measures as the unknown functions to be estimated. The use of q-measure provides a more flexible and powerful way of simplifying the computation of λ-measure used to take account of interaction between the fuzzy sets. This model has been successfully applied to the real life problem of verifying the authenticity of offline signatures.

[1]  M. Sugeno,et al.  Structure identification of fuzzy model , 1988 .

[2]  Madasu Hanmandlu,et al.  Off-line signature verification and forgery detection using fuzzy modeling , 2005, Pattern Recognit..

[3]  Smriti Srivastava,et al.  Control and identification of non-linear systems affected by noise using wavelet network , 2002 .

[4]  George J. Klir,et al.  Fuzzy sets and fuzzy logic - theory and applications , 1995 .

[5]  Yinghua Lin,et al.  A new approach to fuzzy-neural system modeling , 1995, IEEE Trans. Fuzzy Syst..

[6]  M. Sugeno,et al.  Fuzzy Measures and Integrals: Theory and Applications , 2000 .

[7]  Madhusudan Singh,et al.  New fuzzy wavelet neural networks for system identification and control , 2005, Appl. Soft Comput..

[8]  H. Zimmermann,et al.  Fuzzy Set Theory and Its Applications , 1993 .

[9]  L. C. Jang,et al.  On the representation of Choquet integrals of set-valued functions, and null sets , 2000, Fuzzy Sets Syst..

[10]  Vamsi-Krishna Madasu Automatic Bank Check Processing and Authentication using Signature Verification , 2006 .

[11]  Magdi A. Mohamed,et al.  Q-measures: an efficient extension of the Sugeno λ-measure , 2003, IEEE Trans. Fuzzy Syst..

[12]  Hans-Jürgen Zimmermann,et al.  Fuzzy Set Theory - and Its Applications , 1985 .

[13]  Jacek M. Zurada,et al.  Introduction to artificial neural systems , 1992 .

[14]  Kwong-Sak Leung,et al.  A new type of nonlinear integrals and the computational algorithm , 2000, Fuzzy Sets Syst..

[15]  Jung-Hsien Chiang,et al.  Choquet fuzzy integral-based hierarchical networks for decision analysis , 1999, IEEE Trans. Fuzzy Syst..

[16]  H. Yan,et al.  Color image segmentation using fuzzy integral and mountain clustering , 1999, Fuzzy Sets Syst..

[17]  R. Yager Aggregation operators and fuzzy systems modeling , 1994 .

[18]  Hung T. Nguyen,et al.  Fundamentals of Uncertainty Calculi with Applications to Fuzzy Inference , 1994 .

[19]  Michio Sugeno,et al.  A fuzzy-logic-based approach to qualitative modeling , 1993, IEEE Trans. Fuzzy Syst..

[20]  Madasu Hanmandlu,et al.  Structure identification of generalized adaptive neuro-fuzzy inference systems , 2003, IEEE Trans. Fuzzy Syst..

[21]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[22]  Paul D. Gader,et al.  Generalized Choquet fuzzy integral fusion , 2002, Inf. Fusion.

[23]  Michael T. Manry,et al.  LMS learning algorithms: misconceptions and new results on converence , 2000, IEEE Trans. Neural Networks Learn. Syst..

[24]  Lambert Schomaker,et al.  Automatic writer identification using connected-component contours and edge-based features of uppercase Western script , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Michel Grabisch,et al.  Classification by fuzzy integral: performance and tests , 1994, CVPR 1994.