Facial recognition using composite correlation filters designed with multiobjective combinatorial optimization

Andres Cuevas, Victor H. Diaz-Ramirez, Vitaly Kober, Leonardo Trujillo

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

5 Citas (Scopus)

Resumen

Facial recognition is a difficult task due to variations in pose and facial expressions, as well as presence of noise and clutter in captured face images. In this work, we address facial recognition by means of composite correlation filters designed with multi-objective combinatorial optimization. Given a large set of available face images having variations in pose, gesticulations, and global illumination, a proposed algorithm synthesizes composite correlation filters by optimization of several performance criteria. The resultant filters are able to reliably detect and correctly classify face images of different subjects even when they are corrupted with additive noise and nonhomogeneous illumination. Computer simulation results obtained with the proposed approach are presented and discussed in terms of efficiency in face detection and reliability of facial classification. These results are also compared with those obtained with existing composite filters.

Idioma originalInglés
Título de la publicación alojadaApplications of Digital Image Processing XXXVII
EditoresAndrew G. Tescher
EditorialSPIE
ISBN (versión digital)9781628412444
DOI
EstadoPublicada - 2014
EventoApplications of Digital Image Processing XXXVII - San Diego, Estados Unidos
Duración: 18 ago. 201421 ago. 2014

Serie de la publicación

NombreProceedings of SPIE - The International Society for Optical Engineering
Volumen9217
ISSN (versión impresa)0277-786X
ISSN (versión digital)1996-756X

Conferencia

ConferenciaApplications of Digital Image Processing XXXVII
País/TerritorioEstados Unidos
CiudadSan Diego
Período18/08/1421/08/14

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