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Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines.

Lara del Val1, Alberto Izquierdo-Fuente2, Juan J Villacorta3

  • 1Departamento de Ciencia de los Materiales e Ingeniería Metalúrgica, Expresión Gráfica de la Ingeniería, Ingeniería Cartográfica, Geodesia y Fotogrametría, Ingeniería Mecánica e Ingeniería de los Procesos de Fabricación, Área de Ingeniería Mecánica, Universidad de Valladolid, Paseo del Cauce 59, 47011 Valladolid, Spain. lvalpue@eii.uva.es.

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|June 20, 2015
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Summary

A novel acoustic biometric system using Support Vector Machine (SVM) classification improves performance by reducing errors and computational load. This enhanced system optimizes acoustic image processing for better security applications.

Keywords:
acoustic biometric systemacoustic imagespreprocessing techniquessupport vector machine

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Area of Science:

  • Biometrics and Pattern Recognition
  • Signal Processing
  • Machine Learning

Background:

  • Existing acoustic biometric systems often face challenges with classification error and computational demands.
  • The Mean Squared Error (MSE) classifier provided initial insights into system performance limitations.

Purpose of the Study:

  • To develop and implement an improved acoustic biometric system.
  • To enhance classification accuracy while reducing computational burden and storage requirements.
  • To identify optimal algorithms for acoustic biometric processing.

Main Methods:

  • Implemented a new system involving acoustic image preprocessing, parameter extraction, and Support Vector Machine (SVM) classification.
  • Utilized spatial filtering, Gaussian Mixture Model (GMM) segmentation, masking, and binarization for preprocessing.
  • Analyzed classification error sensitivity against computational complexity to select relevant algorithms.

Main Results:

  • Achieved significant improvements in the biometric system's overall performance.
  • Demonstrated a notable reduction in classification error rates.
  • Successfully decreased the computational burden and storage requirements of the system.

Conclusions:

  • The developed acoustic biometric system offers superior performance compared to previous methods.
  • The strategic selection of preprocessing and classification algorithms is crucial for optimizing biometric systems.
  • This enhanced system provides a more efficient and effective solution for acoustic-based identification.