A Dynamic Support Vector Data Description Algorithm Applied to Face Verification
Xiao Jian-hua · 2006
Face verification is essentially a problem of one-class classification or outlier detection.Its goal is to accurately describe one class of objects opposing to a wide range of other objects considered as outliers.Based on the existing research wook on statistical learning theory and kernel methods,this paper analyses the drawbacks of a existing one-class classification algorithm,namely support vector data description(SVDD),on dealing with dynamic samples of face verification.This paper points out that,with the increase of samples,the size of optimization will exceed the memory space of the computer.Consequently,the algorithm will be unable to carry out.For the purpose of reducing the size of optimization,the dynamic support vector data description algorithm(DSVDD) is proposed.The new algorithm only computes the new samples and support vectors in the process of optimization,so that the required operation size and memory space can be reduced in a great degree,which means the real-time and dynamic demands are met for face verification.