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Detection Over Viewpoint via the Object Class Invariant

In this article, we present a new model of object class appearance over viewpoint, based on learning a relationship between scale-invariant image features (e.g. SIFT) and a geometric structure that we refer to as an OCI (object class invariant). The OCI is a perspective invariant defined across instances of an object class, and thereby serves as a common reference frame relating features over viewpoint change and object class. A single probabilistic OCI model can be learned to capture the rich multimodal nature of object class appearance in the presence of viewpoint change, providing an efficient alternative to the popular approach of training a battery of detectors at separate viewpoints and/or poses. Experimentation demonstrates that an OCI model of faces can be learned from a small number of natural, cluttered images, and used to detect faces exhibiting a large degree of appearance variation due to viewpoint change and intra-class variability (i.e. (sun)glasses, ethnicity, expression, etc.)