Interpolation Between Eigenspaces Using Rotation in Multiple Dimensions

摘要

We propose a method for interpolation between eigenspaces. Techniques that represent observed patterns as multivariate normal distribution have actively been developed to make it robust over observation noises. In the recognition of images that vary based on continuous parameters such as camera angles, one cause that degrades performance is training images that are observed discretely while the parameters are varied continuously. The proposed method interpolates between eigenspaces by analogy from rotation of a hyper-ellipsoid in high dimensional space. Experiments using face images captured in various illumination conditions demonstrate the validity and effectiveness of the proposed interpolation method.