摘要
手写签名过程中的书写力包含丰富个人特征.受输入设备所限,目前的在线手写签名认证系统都没有能够充分利用这一信息.基于此,本文提出一种综合利用签名字形和书写力特征的在线签名认证方法.首先,利用一种新型的 F-Tablet 手写板用于获取签名的动态笔迹和书写力信息,并根据速度极小值点对签名进行笔画分段.然后从中提取16维的字形和书写力特征矢量序列用于签名隐马尔可夫模型(HMM)的建立和认证.在基于 F-Tablet手写板建立的签名数据库上的认证实验结果表明书写力特征比字形特征更难以模仿,两种特征相结合可以有效提高系统的识别性能,提出的认证方法的相等错误率(EER)达到3.9%.
Writing forces of signature contain a great many individual characteristics, but in current online handwritten signature verification systems this information is often utilized deficiently because of the limitation of the existing input device. In this paper, an online signature verification algorithm using the shape features and the force features is presented. The dynamic trajectory and the writing force of signature are captured by a novel digital tablet, namely F-Tablet. Each signature is segmented according to its minimum velocity points. Then a 16-dimensional vector of shape and force features is extracted for each segment. The resulting sequence is used for training a HMM to achieve signature verification. The experimental results on our database show that the force feature is more difficult to forge than the shape feature and the combination of the two features can effectively improve the performance. The proposed algorithm has achieved equal error rate （EER） of 3.9%.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2007年第1期95-100,共6页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金No.60475005
国家自然科学基金No.60575058
