SVDD-based method for Face Recognition System

抄録

In face recognition, learning speed of face is very important since the system should be trained again whenever a data set is added. In existing methods, training time increases rapidly with the increase of data, which leads to the difficulty of training in large dataset. Thus, we propose SVDD (Support Vector Domain Description)-based method that can learn a data set of face rapidly and reduce the computational load. And we describe the outline of useful scheme in practical use for face recognition. In experimental results, we show that the training speed of proposed method is much faster than that of other methods. Moreover, it is shown that our face recognition system can improve the accuracy gradually by learning faces incrementally from real world.