Combining Support Vector Machines and Eigenspace Modelling for Multi-View Face Detection

Yongmin Li , Shaogang Gong and Heather Liddell
  1. Introduction
  2. Relavant Publications


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

  1. 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