Analysis of pattern recognition and dimensionality reduction techniques for odor biometrics

Rodríguez Luján, Irene; Bailador del Pozo, Gonzalo; Sánchez Ávila, Carmen; Herrero, Ana y Vidal de Miguel, Guillermo (2013). Analysis of pattern recognition and dimensionality reduction techniques for odor biometrics. "Knowledge-Based Systems", v. 52 ; pp. 279-289. ISSN 0950-7051. https://doi.org/10.1016/j.knosys.2013.08.002.

Descripción

Título: Analysis of pattern recognition and dimensionality reduction techniques for odor biometrics
Autor/es:
  • Rodríguez Luján, Irene
  • Bailador del Pozo, Gonzalo
  • Sánchez Ávila, Carmen
  • Herrero, Ana
  • Vidal de Miguel, Guillermo
Tipo de Documento: Artículo
Título de Revista/Publicación: Knowledge-Based Systems
Fecha: Noviembre 2013
Volumen: 52
Materias:
Escuela: Centro de Domótica Integral (CeDInt) (UPM)
Departamento: Otro
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

In this paper, we analyze the performance of several well-known pattern recognition and dimensionality reduction techniques when applied to mass-spectrometry data for odor biometric identification. Motivated by the successful results of previous works capturing the odor from other parts of the body, this work attempts to evaluate the feasibility of identifying people by the odor emanated from the hands. By formulating this task according to a machine learning scheme, the problem is identified with a small-sample-size supervised classification problem in which the input data is formed by mass spectrograms from the hand odor of 13 subjects captured in different sessions. The high dimensionality of the data makes it necessary to apply feature selection and extraction techniques together with a simple classifier in order to improve the generalization capabilities of the model. Our experimental results achieve recognition rates over 85% which reveals that there exists discriminatory information in the hand odor and points at body odor as a promising biometric identifier.

Más información

ID de Registro: 32322
Identificador DC: http://oa.upm.es/32322/
Identificador OAI: oai:oa.upm.es:32322
Identificador DOI: 10.1016/j.knosys.2013.08.002
URL Oficial: http://www.sciencedirect.com/science/article/pii/S0950705113002323
Depositado por: Memoria Investigacion
Depositado el: 06 Feb 2015 13:18
Ultima Modificación: 05 Feb 2016 17:24
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