DocumentCode
3752250
Title
Detection of facial parts via deformable part model using part annotation
Author
Kazuhiro Nishida;Naoko Enami;Yasuo Ariki
Author_Institution
Graduate School of System Informatics, Kobe University, Japan
fYear
2015
Firstpage
192
Lastpage
195
Abstract
In this paper, we propose a novel method for facial parts detection based on Deformable Part Model (DPM). In DPM, the parts are useful regions to detect the face and do not always correspond to the facial parts such as eye, nose and mouth. We model facial parts as a part filter and use annotation to training the position and size. In addition, we discuss the algorithm to deal with the variation of bounding box in the annotation. Our experimental results show that the proposed algorithm improves DPM for facial parts detection.
Keywords
"Face","Mouth","Nose","Training","Deformable models","Face detection","Electronic mail"
Publisher
ieee
Conference_Titel
Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2015 Asia-Pacific
Type
conf
DOI
10.1109/APSIPA.2015.7415501
Filename
7415501
Link To Document