Robust realtime face recognition and tracking system
- Autores
- Chen, Kai; Zhao, Le Jun
- Año de publicación
- 2009
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- 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.
Facultad de Informática - Materia
-
Ciencias Informáticas
meanshift
svm
wavelet
realtime face detection
realtime face tracking
face recognition
Kalman filter - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc/3.0/
- Repositorio
.jpg)
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/9655
Ver los metadatos del registro completo
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Robust realtime face recognition and tracking systemChen, KaiZhao, Le JunCiencias Informáticasmeanshiftsvmwaveletrealtime face detectionrealtime face trackingface recognitionKalman filterThere 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.Facultad de Informática2009-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf82-88http://sedici.unlp.edu.ar/handle/10915/9655enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Oct09-6.pdfinfo:eu-repo/semantics/altIdentifier/issn/1666-6038info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/3.0/Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T10:23:35Zoai:sedici.unlp.edu.ar:10915/9655Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:23:35.554SEDICI (UNLP) - Universidad Nacional de La Platafalse |
| dc.title.none.fl_str_mv |
Robust realtime face recognition and tracking system |
| title |
Robust realtime face recognition and tracking system |
| spellingShingle |
Robust realtime face recognition and tracking system Chen, Kai Ciencias Informáticas meanshift svm wavelet realtime face detection realtime face tracking face recognition Kalman filter |
| 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 |
| dc.creator.none.fl_str_mv |
Chen, Kai Zhao, Le Jun |
| author |
Chen, Kai |
| author_facet |
Chen, Kai Zhao, Le Jun |
| author_role |
author |
| author2 |
Zhao, Le Jun |
| author2_role |
author |
| dc.subject.none.fl_str_mv |
Ciencias Informáticas meanshift svm wavelet realtime face detection realtime face tracking face recognition Kalman filter |
| topic |
Ciencias Informáticas meanshift svm wavelet realtime face detection realtime face tracking face recognition Kalman filter |
| dc.description.none.fl_txt_mv |
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. Facultad de Informática |
| 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. |
| publishDate |
2009 |
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2009-10 |
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article |
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publishedVersion |
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http://sedici.unlp.edu.ar/handle/10915/9655 |
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http://sedici.unlp.edu.ar/handle/10915/9655 |
| dc.language.none.fl_str_mv |
eng |
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eng |
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openAccess |
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