Combining Support Vector Machines and Eigenspace Modelling for
Multi-View Face Detection
Yongmin Li ,
Shaogang Gong and
Heather
Liddell
- Introduction
- Relavant Publications
Introduction
The eigenface method and the Support Vector Machine (SVM) method are
two widely used techniques in face detection. The former seeks to
estimate the probability distribution of face patterns as a unimodal
Gaussian. It is fast but less accurate since the model may be too
simplistic, especially for multi-view face patterns whose distribution
is usually irregular. On the other hand, the SVM method, which tries
to model the boundary between face and non-face patterns, is more
accurate but slower. In this work, a combination of the two methods is
presented to achieve an improved overall performance by speeding up
the computation and maintain the accuracy on an acceptable level.
Relavant Publications
-
Y. Li, S. Gong, J. Sherrah, and
H. Liddell.
Multi-view face detection using support vector machines and eigenspace
modelling.
In The Fourth International Conference on
Knowledge-Based Intelligent Engineering System & Allied
Technologies, pages 241-244, Brighton, UK, 2000.
Yongmin Li
2001-02-07