In this paper we study the reliability of the methods of Face Recognition on the ground of the precision of Face Detection. We have made an extensive study on methods for feature extraction, feature transformation and classification in the problem of Face Recognition. In this work, we report an experimental comparison of several learning systems coupled with different feature representations. Extensive experiments carried out on the ORL database and YALE-B database, which are the most common benchmarks in this area, prove the advantage of a new approach based on an ensemble of classifiers when compared with other well-known techniques. Our experiments show that combining different face recognition approaches we obtain a very low equal error rate.

A. Lumini , L. Nanni (2005). Combining classifiers to obtain a reliable method for face recognition. MULTIMEDIA CYBERSCAPE JOURNAL, 3, 47-53.

Combining classifiers to obtain a reliable method for face recognition

LUMINI, ALESSANDRA;NANNI, LORIS
2005

Abstract

In this paper we study the reliability of the methods of Face Recognition on the ground of the precision of Face Detection. We have made an extensive study on methods for feature extraction, feature transformation and classification in the problem of Face Recognition. In this work, we report an experimental comparison of several learning systems coupled with different feature representations. Extensive experiments carried out on the ORL database and YALE-B database, which are the most common benchmarks in this area, prove the advantage of a new approach based on an ensemble of classifiers when compared with other well-known techniques. Our experiments show that combining different face recognition approaches we obtain a very low equal error rate.
2005
A. Lumini , L. Nanni (2005). Combining classifiers to obtain a reliable method for face recognition. MULTIMEDIA CYBERSCAPE JOURNAL, 3, 47-53.
A. Lumini ; L. Nanni
File in questo prodotto:
Eventuali allegati, non sono esposti

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/7302
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact