  In this paper we propose a supervised object recognition method using new
global features and inspired by the model of the human primary visual cortex V1
as the semidiscrete roto-translation group $SE(2,N) = \mathbb Z_N\rtimes
\mathbb R^2$. The proposed technique is based on generalized Fourier
descriptors on the latter group, which are invariant to natural geometric
transformations (rotations, translations). These descriptors are then used to
feed an SVM classifier. We have tested our method against the COIL-100 image
database and the ORL face database, and compared it with other techniques based
on traditional descriptors, global and local. The obtained results have shown
that our approach looks extremely efficient and stable to noise, in presence of
which it outperforms the other techniques analyzed in the paper.
