论文标题
基于VQ的快速在线签名识别与时间建模
Fast on-line signature recognition based on VQ with time modeling
论文作者
论文摘要
本文提出了一种用于在线签名识别的多部分矢量量化方法。我们已经使用了MCYT数据库，该数据库由330个用户和25个熟练的伪造组成，每人由5个不同冒名顶替者进行。该数据库大于文献中通常使用的数据库。但是，我们还提供了SVC数据库的结果。 我们提出的系统的表现优于SVC的获胜者，其计算要求比DTW低约47倍。此外，由于向量压缩，我们的系统会改善数据库存储要求，并且更友好地友好，因为不可能使用CodeBook恢复原始签名。 MCYT的实验结果可提供99.76％的识别率和2.46％的EER（熟练的伪造和个人阈值）。 SVC的实验结果是识别率的100％和0％（单个阈值）和0.31％（一般阈值）时使用两段VQ方法。
This paper proposes a multi-section vector quantization approach for on-line signature recognition. We have used the MCYT database, which consists of 330 users and 25 skilled forgeries per person performed by 5 different impostors. This database is larger than those typically used in the literature. Nevertheless, we also provide results from the SVC database. Our proposed system outperforms the winner of SVC with a reduced computational requirement, which is around 47 times lower than DTW. In addition, our system improves the database storage requirements due to vector compression, and is more privacy-friendly as it is not possible to recover the original signature using the codebooks. Experimental results with MCYT provide a 99.76% identification rate and 2.46% EER (skilled forgeries and individual threshold). Experimental results with SVC are 100% of identification rate and 0% (individual threshold) and 0.31% (general threshold) when using a two-section VQ approach.
