title: Classification and Verification of Handwritten Signatures with Time Causal Information Theory Quantifiers.
abstract: 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.
authors.affiliation: 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.|Facultad de Ingeniería y Ciencias Aplicadas, Universidad de los Andes, Santiago, Chile.
authors.forename: Osvaldo A
authors.identifier: http://orcid.org/0000-0002-8002-5341
authors.initials: OA
authors.lastname: Rosso
authors.affiliation: Centro de Ciências Exatas e da Natureza, Departamento de Estatística, Universidade Federal de Pernambuco (UFPE), Recife, PE, Brasil.
authors.forename: Raydonal
authors.initials: R
authors.lastname: Ospina
authors.affiliation: Laboratório de Computação Científica e Análise Numérica, Universidade Federal de Alagoas (UFAL), Maceió, AL, Brazil.
authors.forename: Alejandro C
authors.initials: AC
authors.lastname: Frery
mesh_terms: D001699:Biometry / Q000379:methods*; D019277:Entropy; D006236:Handwriting; D006801:Humans; D007206:Individuality; D007257:Information Theory; D010363:Pattern Recognition, Automated / Q000379:methods*; D012984:Software; D060388:Support Vector Machine