Face Recognition on Datasets of Various Scales
Kexin Xu · Nanotechnology and Precision Engineering · 2007
The nonlinear recognition problem was discussed while increasing varieties within classes or numbers of classes,and the recognition results was compared by using linear and nonliner methods on the face datasets of various scales.Three experiments were carried out using the expression set(330 persons)and the pose set(1 000 persons)from CAS-PEAL face database.The results show that only increasing varieties within classes can improve correct recognition rates using either linear methods or nonlinear methods while the number of classes is invariable and smaller than 300.However,while the number of the classes increases and the varieties within classes keeps invariable,the results by using the linear recognition methods and the nonlinear methods are different.With the number of the classes increasing,the correct recognition rates decrease by using the linear methods,but for the nonlinear method based on kernel they are relatively invariable.Therefore,it is concluded that reasonably selecting methods and designing varieties within classes according to the number of classes are necessary to improve correct recognition rate.Furthermore,the nonlinear method based on kernel is fit for datasets in large scale.