@article{oai:ir.kagoshima-u.ac.jp:00007666,
 author = {NAKAMORI, Seiichi and YAMAMOTO, Naoki},
 journal = {鹿児島大学教育学部研究紀要. 自然科学編, Bulletin of the Faculty of Education, Kagoshima University. Natural science},
 month = {2016-10-28},
 note = {This paper presents a new face recognition method using the QR decomposition. The face recognition method is compared with the face recognition method by the snapshot Principal Component Analysis (PCA) mainly from the viewpoint of the computation time consumed for the face recognition. The recognition is based on the distance, measured by the L_2 norm, between the vector of the projected test face image and the vectors of the projected training face images. Specifically, each image is stored in a vector of size N. Instead of an N x N covariance matrix in the PCA, as in the snapshot PCA method, Eigenspace is created from a P x P covariance matrix, where P is the number of persons or training images. Some pattern recognition examples are shown. It is found that the proposed snapshot QR decomposition method is preferable, in face recognition, to the snapshot PCA method.},
 pages = {43--63},
 title = {A New Face Recognition Method Using QR Decomposition},
 volume = {62},
 year = {}
}