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Sensors (Basel). 2015 Jul 23;15(8):17944-62. doi: 10.3390/s150817944.

Fusion of Visible and Thermal Descriptors Using Genetic Algorithms for Face Recognition Systems.

Author information

1
Escuela de Ingeniería Eléctrica, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362804, Chile. gabriel.hermosilla@ucv.cl.
2
Escuela de Ingeniería Eléctrica, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362804, Chile. francisco.gallardo.s@mail.pucv.cl.
3
Escuela de Ingeniería Eléctrica, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362804, Chile. gonzalo.farias@ucv.cl.
4
Department of Electrical Engineering, University of La Frontera, Temuco 4811230, Chile. cesar.sanmartin@ufrontera.cl.

Abstract

The aim of this article is to present a new face recognition system based on the fusion of visible and thermal features obtained from the most current local matching descriptors by maximizing face recognition rates through the use of genetic algorithms. The article considers a comparison of the performance of the proposed fusion methodology against five current face recognition methods and classic fusion techniques used commonly in the literature. These were selected by considering their performance in face recognition. The five local matching methods and the proposed fusion methodology are evaluated using the standard visible/thermal database, the Equinox database, along with a new database, the PUCV-VTF, designed for visible-thermal studies in face recognition and described for the first time in this work. The latter is created considering visible and thermal image sensors with different real-world conditions, such as variations in illumination, facial expression, pose, occlusion, etc. The main conclusions of this article are that two variants of the proposed fusion methodology surpass current face recognition methods and the classic fusion techniques reported in the literature, attaining recognition rates of over 97% and 99% for the Equinox and PUCV-VTF databases, respectively. The fusion methodology is very robust to illumination and expression changes, as it combines thermal and visible information efficiently by using genetic algorithms, thus allowing it to choose optimal face areas where one spectrum is more representative than the other.

KEYWORDS:

face recognition; fusion descriptors; genetic algorithms; visible and infrared spectrum

PMID:
26213932
PMCID:
PMC4570301
DOI:
10.3390/s150817944
[Indexed for MEDLINE]
Free PMC Article

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