Face Recognition with Only One Training Sample

In this paper, we compare the face recognition performance for five different methods with using only one training sample. Firstly, we investigate the singular value decomposition (SVD) of the face image and propose an augmenting algorithm via using only one sample to generate a group of training samples. Then we implement the methods of face recognition with discrete cosine transform (DCT) and two dimensional principal component analysis (2DPCA). Secondly, we implement face recognition approach via DCT directly with one training sample. Thirdly, we primarily use DCT to generate some low-frequency matrices in frequency domain and then converted into the spatial domain as independent training images. Then, 2DPCA will be used for face recognition. Finally, we use DCT to generate some low-frequency matrices in frequency domain and use DCT to do face recognition. Experiments on the AMP and Yale face database show that the approach DCT+2DPCA produces better results on the AMP database. The approach SVD+2DPCA produces better result on Yale database.