In this paper,we present a strategy to implement multi-pose face detection in compressed domain.The strategy extracts firstly feature vectors from DCT domain,and then uses a boosting algorithm to build classificrs to ...In this paper,we present a strategy to implement multi-pose face detection in compressed domain.The strategy extracts firstly feature vectors from DCT domain,and then uses a boosting algorithm to build classificrs to distinguish faces and non-faces.Moreover,to get more accurate results of the face detection,we present a kernel function and a linear combination to build incrementally the strong classifiers based on the weak classifiers.Through comparing and analyzing results of some experiments on the synthetic data and the natural data,we can get more satisfied results by the strong classifiers than by the weak classifies.展开更多
基金Supported by the National863Prugram(2002AA11101)Open Fand of State Teehnology Center uf Mult-media Software Engineering(621-273128)
文摘In this paper,we present a strategy to implement multi-pose face detection in compressed domain.The strategy extracts firstly feature vectors from DCT domain,and then uses a boosting algorithm to build classificrs to distinguish faces and non-faces.Moreover,to get more accurate results of the face detection,we present a kernel function and a linear combination to build incrementally the strong classifiers based on the weak classifiers.Through comparing and analyzing results of some experiments on the synthetic data and the natural data,we can get more satisfied results by the strong classifiers than by the weak classifies.
