Face Recognition in Subspaces. 601 Biometric Technologies Course. Abstract. Images of faces, represented as high-dimensional pixel arrays, belong to a manifold (distribution) of a low dimension.
601 Biometric Technologies Course
x~Ay, AT A ≠I, P(y) ~ Π p(yi)
Fig. (a) PCA basis (linear, ordered and orthogonal)
(b) ICA basis (linear, unordered, and nonorthogonal)
(c) Principal curve (parameterized nonlinear manifold). The circle shows the data mean.