Abstract:An online signature verification algorithm is presented based on feature extraction and feature selection. A novel digital tablet, called F-Tablet, is used to capture the signature information. The tablet can capture both shape series and five-dimensional forces. Total 188 features are extracted from each signature and then divided into three classes. Then, the weight function of features F is defined and the 188 features are sorted according to the F values. With different thresholds, different feature sets are obtained. The SVM is used to train the selected feature sets in the training process and the signatures are verified by the trained models. The proposed algorithm achieves false rejection rate (FRR) of 1.2% and false acceptance rate (FAR) of 3.7%.
[1] Meng Ming, Wu Zhongcheng, Yu Yong, et al. On-line Signature Verification Based on Segment Features and HMM. Pattern Recognition and Artificial Intelligence, 2007, 20(1): 95-100 (in Chinese) (孟 明,吴仲城,余 勇,等.基于笔段特征和HMM的在线签名认证方法研究.模式识别与人工智能, 2007, 20(1): 95-100) [2] Griess F D. Project Report: On-line Signature Verification [EB-OL]. [2000-05-01]. http://www.cse.msu.edu/publications/tech/TR/MSUCSE-00-15.ps [3] Zhang D, Jing Xiaoyuan, Yang Jian. Biometric Images Discrimination Technologies. Hershey, USA: Idea Group Publishing, 2006 [4] Li Bin, Zhang D, Wang Kuanquan. On-line Signature Verification Based on Null Component Analysis and Principal Component Analysis. Pattern Analysis Applications, 2006, 8(4): 345-356 [5] Zhang Liping, Wu Zhongcheng. On-line Handwritten Signature Verification Algorithm Based on Wavelet Transform to Extract Characteristic Points. Journal of Computer Applications, 2006, 26(10): 2496-2498 (in Chinese) (张丽平,吴仲城.基于小波变换提取特征点的在线手写签名认证算法.计算机应用, 2006, 26(10): 2496-2498) [6] Wu Zhongcheng, Fang Ping, Meng Ming, et al. A Novel Force Sensitive Tablet for Handwriting Information Acquisition // Proc of the 5th Chinese Conference on Biometric Recognition. Guangzhou, China, 2004: 654-662 [7] Plamondon R, Lorette G. Automatic Signature Verification and Writer Identification — the State of the Art. Pattern Recognition, 1989, 22(2): 107-131 [8] Nalwa V S. Automatic On-line Signature Verification. Proc of the IEEE, 1997, 85(2): 215-239 [9] Ma Mingming, Wijesoma W S, Sung E. An Automatic On-line Signature Verification System Based on Three Modals. IEEE Trans on Industrial Electronics, 2000, 4(5): 20-25 [10] Kim S H, Park M S, Kim J. Applying Personalized Weights to a Feature Set for On-line Signature Verification // Proc of the 3rd International Conference on Document Analysis and Recognition. Montreal, Canada, 1995, Ⅱ: 882-885 [11] Plamondon R, Srihari S N. On-line and Off-line Handwriting Recognition: A Comprehensive Survey. IEEE Trans on Pattern Recognition and Machine Intelligence, 2000, 22(1):63-84 [12] Sun Jixiang. Modern Pattern Recognition. Changsha, China: National University of Defense Technology Press, 2002 (in Chinese) (孙即祥.现代模式识别.长沙:国防科技大学出版社, 2002)