Publication detail

New Methods for Face Recognition

ŠŤASTNÝ, J. ŠKORPIL, V.

Original Title

New Methods for Face Recognition

Czech Title

Nové metody rezpoznání obličeje

English Title

New Methods for Face Recognition

Type

conference paper

Language

en

Original Abstract

The paper describes the PCA (Principal Component Analysis) method and its comparison with a method using a neural network with RBF (Radial Basis Function) for classification over PCA data. The PCA method has a very solid resolution power for a small database and in this paper a database sample is described that contains 4000 images for 40 different people. Using the PCA method is suitable.

Czech abstract

Příspěvek popisuje metodu PCA (Principal Component Analysis) a její porovnání s metodou používající RBF (Radial Basis Function) neuronovou síť pro klasifikaci PCA dat. Metoda PCA je výhodné řešení pro malé databáze a tento příspěvek popisuje databázi obsahující 4000 obrazů 40 rozdílných osob. Použití PCA je vhodné.

English abstract

The paper describes the PCA (Principal Component Analysis) method and its comparison with a method using a neural network with RBF (Radial Basis Function) for classification over PCA data. The PCA method has a very solid resolution power for a small database and in this paper a database sample is described that contains 4000 images for 40 different people. Using the PCA method is suitable.

Keywords

Algorithms, Image analysis, Neural networks, Object recognition

RIV year

2010

Released

20.08.2010

Publisher

Asszistencia

Location

Baden near Vienna

ISBN

978-963-88981-0-4

Book

Proceedings of 33th International Conference on Telecommunications and Signal Processing

Edition number

1

Pages from

156

Pages to

159

Pages count

4

BibTex


@inproceedings{BUT35609,
  author="Jiří {Šťastný} and Vladislav {Škorpil}",
  title="New Methods for Face Recognition",
  annote="The paper describes the PCA (Principal Component Analysis) method and its comparison with a method using a neural network with RBF (Radial Basis Function) for classification over PCA data. The PCA method has a very solid resolution power for a small database and in this paper a database sample is described that contains 4000 images for 40 different people. Using the PCA method is suitable.",
  address="Asszistencia",
  booktitle="Proceedings of 33th International Conference on Telecommunications and Signal Processing",
  chapter="35609",
  howpublished="print",
  institution="Asszistencia",
  year="2010",
  month="august",
  pages="156--159",
  publisher="Asszistencia",
  type="conference paper"
}