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.