Face and iris localization using templates designed by particle swarm optimization
Author
dc.contributor.author
Pérez Flores, Claudio
es_CL
Author
dc.contributor.author
Aravena, Carlos
es_CL
Author
dc.contributor.author
Vallejos, Juan I.
es_CL
Author
dc.contributor.author
Estévez Valencia, Pablo
es_CL
Author
dc.contributor.author
Held Barrandeguy, Claudio
Admission date
dc.date.accessioned
2014-01-09T18:46:37Z
Available date
dc.date.available
2014-01-09T18:46:37Z
Publication date
dc.date.issued
2010
Cita de ítem
dc.identifier.citation
Pattern Recognition Letters 31 (2010) 857–868
en_US
Identifier
dc.identifier.other
doi:10.1016/j.patrec.2009.12.029
Identifier
dc.identifier.uri
https://repositorio.uchile.cl/handle/2250/126131
General note
dc.description
Artículo de publicación ISI
en_US
Abstract
dc.description.abstract
Face and iris localization is one of the most active research areas in image understanding for new applications
in security and theft prevention, as well as in the development of human–machine interfaces. In
the past, several methods for real-time face localization have been developed using face anthropometric
templates which include face features such as eyes, eyebrows, nose and mouth. It has been shown that
accuracy in face and iris localization is crucial to face recognition algorithms. An error of a few pixels in
face or iris localization will produce significant reduction in face recognition rates. In this paper, we present
a new method based on particle swarm optimization (PSO) to generate templates for frontal face
localization in real time. The PSO templates were tested for face localization on the Yale B Face Database
and compared to other methods based on anthropometric templates and Adaboost. Additionally, the PSO
templates were compared in iris localization to a method using combined binary edge and intensity
information in two subsets of the AR face database, and to a method based on SVM classifiers in a subset
of the FERET database. Results show that the PSO templates exhibit better spatial selectivity for frontal
faces resulting in a better performance in face localization and face size estimation. Correct face localization
reached a rate of 97.4% on Yale B which was higher than 96.2% obtained with the anthropometric
templates and much better than 60.5% obtained with the Adaboost face detection method. On the AR face
subsets, different disparity errors were considered and for the smallest error, a 100% correct detection
was reached in the AR-63 subset and 99.7% was obtained in the AR-564 subset. On the FERET subset a
detection rate of 96.6% was achieved using the same criteria. In contrast to the Adaboost method, PSO
templates were able to localize faces on high-contrast or poorly illuminated environments. Additionally,
in comparison with the anthropometric templates, the PSO templates have fewer pixels, resulting in a
40% reduction in processing time thus making them more appropriate for real-time applications.