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Conference Paper: A hierarchical FloatBoost and MLP classifier for mobile phone embedded eye location system

TitleA hierarchical FloatBoost and MLP classifier for mobile phone embedded eye location system
Authors
Issue Date2006
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2006, v. 3972 LNCS, p. 20-25 How to Cite?
AbstractThis paper is focused on cellular phone embedded eye location system. The proposed eye detection system is based on a hierarchy cascade FloatBoost classifier combined with an MLP neural net post classifier. The system firstly locates the face and eye candidates' areas in the whole image by a hierarchical FloatBoost classifier. Then geometrical and relative position information of eye-pair and the face are extracted. These features are input to a MLP neural net post classier to arrive at an eye/non-eye decision. Experimental results show that our cellular phone embedded eye detection system can accurately locate double eyes with less computational and memory cost. It runs at 400ms per image of size 256×256 pixels with high detection rates on a SANYO cellular phone with ARM926EJ-S processor that lacks floating-point hardware. © Springer-Verlag Berlin Heidelberg 2006.
Persistent Identifierhttp://hdl.handle.net/10722/212869
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252

 

DC FieldValueLanguage
dc.contributor.authorChen, Dan-
dc.contributor.authorTang, Xusheng-
dc.contributor.authorOu, Zongying-
dc.contributor.authorXi, Ning-
dc.date.accessioned2015-07-28T04:05:16Z-
dc.date.available2015-07-28T04:05:16Z-
dc.date.issued2006-
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2006, v. 3972 LNCS, p. 20-25-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/212869-
dc.description.abstractThis paper is focused on cellular phone embedded eye location system. The proposed eye detection system is based on a hierarchy cascade FloatBoost classifier combined with an MLP neural net post classifier. The system firstly locates the face and eye candidates' areas in the whole image by a hierarchical FloatBoost classifier. Then geometrical and relative position information of eye-pair and the face are extracted. These features are input to a MLP neural net post classier to arrive at an eye/non-eye decision. Experimental results show that our cellular phone embedded eye detection system can accurately locate double eyes with less computational and memory cost. It runs at 400ms per image of size 256×256 pixels with high detection rates on a SANYO cellular phone with ARM926EJ-S processor that lacks floating-point hardware. © Springer-Verlag Berlin Heidelberg 2006.-
dc.languageeng-
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.titleA hierarchical FloatBoost and MLP classifier for mobile phone embedded eye location system-
dc.typeConference_Paper-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-33745904434-
dc.identifier.volume3972 LNCS-
dc.identifier.spage20-
dc.identifier.epage25-
dc.identifier.eissn1611-3349-

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