Research of Face Recognition with FLDA from Single Sample per Person
NI Jianhon · Video Engineering · 2013
Usually,Fishier Linear Discriminative Analysis(FLDA) can be effective in face recognition when each person has multiple samples(MMSP).However,it will not be used when each person has only one training sample(SSPP) because the intra-class metric is zero.To address this problem,a novel method to estimate the intra-class scatter metric is proposed.By using the Singular Value Decomposition(SVD),firstly,face image is decomposed into two parts,and then they are used to estimate intra-class and inter-class scatter metrics,which making the traditional FLDA can be applied to SSPP task.Experiments on the ORL and Yale face database show that the proposed method can achieve better recognition accuracy than many common solutions to the SSPP problem.