A two-layer support vector classifier model with rejection feature is proposed in this paper.Firstly the sphere support vectors of each class to describe the distribution of the sample were obtained by searching all the sphere boundaries containing the samples of each class.Then the input pattern of no-object classes could be rejected by the first support vector domain description (SVDD).If a pattern is accepted by the first SVDD

the second layer of support vector classifier (SVC) with maximum margin between two classes will be used for classification.In addition

Instead of the traditional quadratic programming

multiplicative iterative updates rule is used to solve the optimizing problems in SVDD of the first layer and the SVC in second layer.Compared to the tradition algorithm of the support vector machine

the new method improves greatly the computational speed of optimization.Experimental results demonstrate that the method of two-Layer support vector classifier with Rejection Feature is feasible and it could be applicable in many real pattern recognition fields.