Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/127128
Título: Online Signature Recognition: A Biologically Inspired Feature Vector Splitting Approach
Autores/as: Faundez-Zanuy, Marcos
Diaz, Moises 
Ferrer Ballester, Miguel Ángel 
Clasificación UNESCO: 3307 Tecnología electrónica
Palabras clave: Biometrics
Dynamic Time Warping
E-Security
Online Signature
Vector Quantization
Fecha de publicación: 2023
Publicación seriada: Cognitive Computation 
Resumen: This research introduces an innovative approach to explore the cognitive and biologically inspired underpinnings of feature vector splitting for analyzing the significance of different attributes in e-security biometric signature recognition applications. Departing from traditional methods of concatenating features into an extended set, we employ multiple splitting strategies, aligning with cognitive principles, to preserve control over the relative importance of each feature subset. Our methodology is applied to three diverse databases (MCYT100, MCYT300, and SVC) using two classifiers (vector quantization and dynamic time warping with one and five training samples). Experimentation demonstrates that the fusion of pressure data with spatial coordinates (x and y) consistently enhances performance. However, the inclusion of pen-tip angles in the same feature set yields mixed results, with performance improvements observed in select cases. This work delves into the cognitive aspects of feature fusion, shedding light on the cognitive relevance of feature vector splitting in e-security biometric applications.
URI: http://hdl.handle.net/10553/127128
ISSN: 1866-9956
DOI: 10.1007/s12559-023-10205-9
Fuente: Cognitive Computation [ISSN 1866-9956], septiembre 2023
Colección:Artículos
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