Abstract

A new three-order piecewise function was used to smoothen the model of Support Vector Machine( SVM) and a Third-order Piecewise Smooth SVM( TPSSVM) was proposed. By theory analyzing, approximation accuracy of the smooth function to the plus function is higher than that of the available. When dealing with the multi-class problem, a coding method of multi-class classification based on one-against-rest was proposed. Principal Component Analysis( PCA) was employed to extract the main features of face image set, and multi-class classification of smooth SVM was used for face recognition. The experimental results on ORL and FERET face databases show that the recognition rate of smooth SVM for multi-class classification is better than the traditional identification methods.