@InProceedings{LucenaOlivVeloPere:2017:ImFaDe,
author = "Lucena, Oeslle and Oliveira, {\'{\I}}talo de P. and Veloso,
Luciana and Pereira, Eanes",
affiliation = "{University of Campinas} and {Federal University of Campina
Grande} and {Federal University of Campina Grande} and {Federal
University of Campina Grande}",
title = "Improving Face Detection Performance by Skin Detection
Post-Processing",
booktitle = "Proceedings...",
year = "2017",
editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and
Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and
Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba,
Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo
and Vital, Creto and Pagot, Christian Azambuja and Petronetto,
Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
publisher = "IEEE Computer Society",
address = "Los Alamitos",
keywords = "Face detection, Skin detection, Performance Improvement,
Post-processing.",
abstract = "Face detection is already incorporated in many biometrics and
surveillance applications. Therefore, the reduction of false
detections is a priority in those systems. However, face detection
is still challenging. Many factors, such as pose variation and
complex backgrounds, contribute to false detections. Besides, the
fidelity of a true detection, measured by precision rate, is a
concern in content-based information retrieval. Following those
issues, combinations of methods are developed focusing on
balancing the trade-off between hit-rate and miss-rate. In this
paper, we present an approach that improves face detection based
on a post-processing of skin features. Our method enhanced the
performance of weak detectors using a straightforward and low
complex skin percentage threshold constraint. Furthermore, we also
present a statistical analysis comparing our approach and two face
detectors, under two different conditions for skin detection
training, using a robust dataset for testing. The experimental
results showed a significant drop in the number of false
positives, reducing in 53%, while the precision rate was elevated
in almost 5% when the Viola-Jones approach was used as face
detector.",
conference-location = "Niter{\'o}i, RJ, Brazil",
conference-year = "17-20 Oct. 2017",
doi = "10.1109/SIBGRAPI.2017.46",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2017.46",
language = "en",
ibi = "8JMKD3MGPAW/3PFMEB5",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3PFMEB5",
targetfile = "SIBGRAPI_paper(2).pdf",
urlaccessdate = "2024, May 01"
}