Rosso Osvaldo A, Ospina Raydonal, Frery Alejandro C
Instituto de Física, Universidade Federal de Alagoas (UFAL), Maceió, AL, Brazil.
Instituto Tecnológico de Buenos Aires (ITBA), and CONICET, Ciudad Autónoma de Buenos Aires, Argentina.
PLoS One. 2016 Dec 1;11(12):e0166868. doi: 10.1371/journal.pone.0166868. eCollection 2016.
We present a new approach for handwritten signature classification and verification based on descriptors stemming from time causal information theory. The proposal uses the Shannon entropy, the statistical complexity, and the Fisher information evaluated over the Bandt and Pompe symbolization of the horizontal and vertical coordinates of signatures. These six features are easy and fast to compute, and they are the input to an One-Class Support Vector Machine classifier. The results are better than state-of-the-art online techniques that employ higher-dimensional feature spaces which often require specialized software and hardware. We assess the consistency of our proposal with respect to the size of the training sample, and we also use it to classify the signatures into meaningful groups.
我们提出了一种基于源于时间因果信息理论的描述符的手写签名分类和验证新方法。该方法使用香农熵、统计复杂度以及在签名水平和垂直坐标的班特与庞贝符号化上评估的费希尔信息。这六个特征易于快速计算，并且是一类支持向量机分类器的输入。结果优于采用通常需要专门软件和硬件的高维特征空间的现有在线技术。我们评估了我们的方法相对于训练样本大小的一致性，并且还使用它将签名分类为有意义的组。
