Online Signature Verification SystemBased on Support Vector Data Description
ZOU Jie1, WU Zhong-Cheng1,2
1.Automation Control Group, High Magnetic Field Laboratory, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031 2.Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031
Abstract：An online signature verification system is proposed based on support vector data description (SVDD). Firstly, correspondences of the critical points in signatures are confirmed by bidirectional backward-merging dynamic time wrapping algorithm. Then, subtle differences in the local are calculated by classical dynamic time wrapping algorithm. Feature selection principle based on mean and deviation minimization is proposed. Finally, the classifiers are designed using SVDD. To obtain better result, m-fold cross validation and genetic algorithm are used to seek optimal parameters of SVDD. The average equal error rate for skill forge signatures on SVC2004 signatures database is 4.25%.
[1] Plamondon R, Lorette G. Automatic Signature Verification and Writer Identification-the State of the Art. Pattern Recognition Letters, 1989, 22(2): 107-131 [2] Feng Hao, Wah C C. Online Signature Verification Using a New Extreme Points Warping Technique. Pattern Recognition Letters, 2003, 24(16): 2943-2951 [3] Zhang k, Pratikakis I, Cornelis J, et al. Using Landmarks to Establish a Point-to-Point Correspondence between Signatures. Pattern Analysis an Applications, 2000, 3(1): 69- 75 [4] Lee J, Yoon H S, Jung Soh, et al. Using Geometric Extrema for Segment-to-Segment Characteristics Comparison in Online Signature Verification. Pattern Recognition, 2004, 37(1): 92-103 [5] Li Bin, Zhang D, Wang Kuanquan. Improved Critical Point Correspondence for On-Line Signature Verification. International Journal of Information Technology, 2006, 12(7): 45-56 [6] Lei Hansheng, Govindaraju V. A Comparative Study on the Consistency of Features in On-Line Signature Verification. Pattern Recognition Letters, 2005, 26(15): 2483-2489 [7] Jain A K, Griess F D, Connell S D. On-Line Signature Verification. Pattern Recognition, 2002, 35(12): 2963-2972 [8] Guru D S, Prakash H N. Online Signature Verification and Recognition: An Approach Based on Symbolic Representation. IEEE Trans on Pattern Analysis and Machine Intelligence, 2009, 31(6): 1059-1073 [9] Draouhard J P, Sabourin R, Godbout M. A Neural Network Approaches to On-Line Signature Verification Using Directional PDF. Pattern Recognition, 1996, 29(3): 415-424 [10] Bajaj R, Chaudhary S. Signature Verification Using Multiple Neural Classifiers. Pattern Recognition, 1997, 30(1): 1-7 [11] Kholmatov A, Yanikoglu B. Identity Authentication Using Improved Online Signature Verification Method. Pattern Recognition Letters, 2005, 26(15): 2400-2408 [12] Tax D M J, Duin R P W. Support Vector Domain Description. Pattern Recognition Letters, 1999, 20(11/12/13): 1191-1199 [13] Yeung D Y, Chang H, Xiong Yimin, et al. SVC2004: First International Signature Verification Competition // Proc of the 1st International Conference on Biometric Authentication. Hongkong, China, 2004: 16-22 [14] Sakoe H, Chiba S. Dynamic Programming Algorithm Optimization for Spoken Word Recognition. IEEE Trans on Acoustics, Speech and Signal Processing, 1978, 26(1): 43-49 [15] Brault J J, Plamondon R. Segmenting Handwritten Signatures at Their Perceptually Important Points . IEEE Trams on Pattern Analysis and Machine Intelligence, 1993, 15(9): 953-957 [16] Cong Shuang. Neural Network Fuzzy System and Applications in Motion Control. Hefei, China: Press of University of Science and Technology of China, 2001 (in Chinese) (丛 爽.神经网络、模糊系统及其在运动控制中的应用.合肥:中国科学技术大学出版社, 2001) [17] Van Bao L, Garcia-Salicetti S, Dorizzi B. On Using the Viterbi Path Along with HMM Likelihood Information for Onling Signature Verification. IEEE Trans on Systems, Man, and Cybernetics, 2007, 37(5): 1237-1247
2011, Vol. 24 
