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dc.date.accessioned 2009-10-09T13:11:56Z
dc.date.available 2009-10-09T03:00:00Z
dc.date.issued 2009 es
dc.identifier.uri http://hdl.handle.net/10915/9655
dc.description.abstract There s some very important meaning in the study of realtime face recognition and tracking system for the video monitoring and artifical vision. The current method is still very susceptible to the illumination condition, non-real time and very common to fail to track the target face especially when partly covered or moving fast. In this paper, we propose to use Boosted Cascade combined with skin model for face detection and then in order to recognize the candidate faces, they will be analyzed by the hybrid Wavelet, PCA (principle component analysis) and SVM (support vector machine) method. After that, Meanshift and Kalman filter will be invoked to track the face. The experimental results show that the algorithm has quite good performance in terms of real-time and accuracy. es
dc.format.extent 7 p. es
dc.language en es
dc.title Robust realtime face recognition and tracking system es
dc.type Articulo es
sedici.identifier.uri http://journal.info.unlp.edu.ar/journal/journal26/papers/JCST-Oct09-6.pdf es
sedici.creator.person Chen, Kai es
sedici.creator.person Zhao, Le Jun es
sedici.subject.materias Ciencias Informáticas es
sedici.subject.descriptores Informática es
sedici.subject.descriptores Aplicación informática es
sedici.subject.keyword PCA; meanshift; Kalman filter; svm; wavelet; Realtime face detection; Realtime face tracking; face recognition es
sedici.description.fulltext true es
mods.originInfo.place Facultad de Informática es
sedici.subtype Articulo es
sedici.rights.license Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
sedici.rights.uri http://creativecommons.org/licenses/by-nc/3.0/
sedici.description.peerReview peer-review es
sedici2003.identifier ARG-UNLP-ART-0000005282 es
sedici.relation.journalTitle Journal of Computer Science & Technology es
sedici.relation.journalVolumeAndIssue vol. 9, no. 2 es


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