Proposing the novelty classifier for face recognition
Rev. bras. eng. biomed
; 30(4): 301-311, Oct.-Dec. 2014. ilus, graf, tab
Article
en En
| LILACS
| ID: lil-732829
Biblioteca responsable:
BR1.1
ABSTRACT
INTRODUCTION:
Face recognition, one of the most explored themes in biometry, is used in a wide range of applications access control, forensic detection, surveillance and monitoring systems, and robotic and human machine interactions. In this paper, a new classifier is proposed for face recognition the novelty classifier.METHODS:
The performance of a novelty classifier is compared with the performance of the nearest neighbor classifier. The ORL face image database was used. Three methods were employed for characteristic extraction principal component analysis, bi-dimensional principal component analysis with dimension reduction in one dimension and bi-dimensional principal component analysis with dimension reduction in two directions.RESULTS:
In identification mode, the best recognition rate with the leave-one-out strategy is equal to 100%. In the verification mode, the best recognition rate was also 100%. For the half-half strategy, the best recognition rate in the identification mode is equal to 98.5%, and in the verification mode, 88%.CONCLUSION:
For face recognition, the novelty classifier performs comparable to the best results already published in the literature, which further confirms the novelty classifier as an important pattern recognition method in biometry.
Texto completo:
1
Colección:
01-internacional
Base de datos:
LILACS
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
Rev. bras. eng. biomed
Asunto de la revista:
ENGENHARIA BIOMEDICA
Año:
2014
Tipo del documento:
Article
País de afiliación:
Brasil