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%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2017/08.21.00.14
%2 sid.inpe.br/sibgrapi/2017/08.21.00.14.15
%T Improving Face Detection Performance by Skin Detection Post-Processing
%D 2017
%8 Oct. 17-20, 2017
%A Lucena, Oeslle,
%A Oliveira, Ítalo de P.,
%A Veloso, Luciana,
%A Pereira, Eanes,
%@affiliation University of Campinas
%@affiliation Federal University of Campina Grande
%@affiliation Federal University of Campina Grande
%@affiliation Federal University of Campina Grande
%E Torchelsen, Rafael Piccin,
%E Nascimento, Erickson Rangel do,
%E Panozzo, Daniele,
%E Liu, Zicheng,
%E Farias, Mylène,
%E Viera, Thales,
%E Sacht, Leonardo,
%E Ferreira, Nivan,
%E Comba, João Luiz Dihl,
%E Hirata, Nina,
%E Schiavon Porto, Marcelo,
%E Vital, Creto,
%E Pagot, Christian Azambuja,
%E Petronetto, Fabiano,
%E Clua, Esteban,
%E Cardeal, Flávio,
%B Conference on Graphics, Patterns and Images, 30 (SIBGRAPI)
%C Niterói, RJ
%S Proceedings
%I IEEE Computer Society
%J Los Alamitos
%K Face detection, Skin detection, Performance Improvement, Post-processing.
%X 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.
%@language en
%3 SIBGRAPI_paper(2).pdf


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