Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Last Update2017: administrator
Metadata Last Update2020: administrator
Citation KeyLucenaOlivVeloPere:2017:ImFaDe
TitleImproving Face Detection Performance by Skin Detection Post-Processing
DateOct. 17-20, 2017
Access Date2021, Jan. 21
Number of Files1
Size758 KiB
Context area
Author1 Lucena, Oeslle
2 Oliveira, Ítalo de P.
3 Veloso, Luciana
4 Pereira, Eanes
Affiliation1 University of Campinas
2 Federal University of Campina Grande
3 Federal University of Campina Grande
4 Federal University of Campina Grande
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2017-08-21 00:19:32 :: -> administrator :: 2017
2020-02-19 02:01:28 :: administrator -> :: 2017
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
KeywordsFace detection, Skin detection, Performance Improvement, Post-processing.
AbstractFace 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.
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Target FileSIBGRAPI_paper(2).pdf
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Next Higher Units8JMKD3MGPAW/3PJT9LS
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