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 
            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, 
             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 
  conference-location = "Niter{\'o}i, RJ",
      conference-year = "Oct. 17-20, 2017",
             language = "en",
           targetfile = "SIBGRAPI_paper(2).pdf",
        urlaccessdate = "2021, Jan. 25"