A near-planar object seen from different viewpoints results in differently deformed images. Under some assumptions, viewpoint changes can be modeled by affine transformations. Shape features that are affine-invariant (af-in) must remain constant with the changes of the viewpoint. Similarly, shape similarity metrics that are af-in must rate the difference between two shapes, regardless of their viewpoints. Af-in shape features and similarity metrics can be used for the shape classification and retrieval. In this paper, we propose a new set of af-in shape features and similarity metrics. They are based on the area matrix, a structure that contains multiscale information about the shape. Experimental results indicate that the proposed techniques are robust to viewpoint changes and can rate correctly the dissimilarities between the shapes.