  Due to advancements in digital cameras, it is easy to gather multiple images
(or videos) from an object under different conditions. Therefore, image-set
classification has attracted more attention, and different solutions were
proposed to model them. A popular way to model image sets is subspaces, which
form a manifold called the Grassmann manifold. In this contribution, we extend
the application of Generalized Relevance Learning Vector Quantization to deal
with Grassmann manifold. The proposed model returns a set of prototype
subspaces and a relevance vector. While prototypes model typical behaviours
within classes, the relevance factors specify the most discriminative principal
vectors (or images) for the classification task. They both provide insights
into the model's decisions by highlighting influential images and pixels for
predictions. Moreover, due to learning prototypes, the model complexity of the
new method during inference is independent of dataset size, unlike previous
works. We applied it to several recognition tasks including handwritten digit
recognition, face recognition, activity recognition, and object recognition.
Experiments demonstrate that it outperforms previous works with lower
complexity and can successfully model the variation, such as handwritten style
or lighting conditions. Moreover, the presence of relevances makes the model
robust to the selection of subspaces' dimensionality.
