Abstract 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.
Interpolation Between Eigenspaces Using Rotation in Multiple Dimensions
Computer Vision – ACCV 2007 ; 10 ; 774-783
Lecture Notes in Computer Science ; 4844 , 10
2007-01-01
10 pages
Article/Chapter (Book)
Electronic Resource
English
Face Recognition , Face Image , Training Image , Rotation Matrix , Illumination Condition Computer Science , Image Processing and Computer Vision , Computer Imaging, Vision, Pattern Recognition and Graphics , Pattern Recognition , Artificial Intelligence (incl. Robotics) , Biometrics , Algorithm Analysis and Problem Complexity
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