Robust realtime face recognition and tracking system
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...
Guardado en:
| Autores principales: | , |
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| Formato: | Articulo |
| Lenguaje: | Inglés |
| Publicado: |
2009
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/9655 http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Oct09-6.pdf |
| Aporte de: |
| id |
I19-R120-10915-9655 |
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| record_format |
dspace |
| institution |
Universidad Nacional de La Plata |
| institution_str |
I-19 |
| repository_str |
R-120 |
| collection |
SEDICI (UNLP) |
| language |
Inglés |
| topic |
Ciencias Informáticas meanshift svm wavelet realtime face detection realtime face tracking face recognition Kalman filter |
| spellingShingle |
Ciencias Informáticas meanshift svm wavelet realtime face detection realtime face tracking face recognition Kalman filter Chen, Kai Zhao, Le Jun Robust realtime face recognition and tracking system |
| topic_facet |
Ciencias Informáticas meanshift svm wavelet realtime face detection realtime face tracking face recognition Kalman filter |
| description |
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. |
| format |
Articulo Articulo |
| author |
Chen, Kai Zhao, Le Jun |
| author_facet |
Chen, Kai Zhao, Le Jun |
| author_sort |
Chen, Kai |
| title |
Robust realtime face recognition and tracking system |
| title_short |
Robust realtime face recognition and tracking system |
| title_full |
Robust realtime face recognition and tracking system |
| title_fullStr |
Robust realtime face recognition and tracking system |
| title_full_unstemmed |
Robust realtime face recognition and tracking system |
| title_sort |
robust realtime face recognition and tracking system |
| publishDate |
2009 |
| url |
http://sedici.unlp.edu.ar/handle/10915/9655 http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Oct09-6.pdf |
| work_keys_str_mv |
AT chenkai robustrealtimefacerecognitionandtrackingsystem AT zhaolejun robustrealtimefacerecognitionandtrackingsystem |
| bdutipo_str |
Repositorios |
| _version_ |
1764820492241338369 |