  In this paper, we propose a method for image-set classification based on
convex cone models, focusing on the effectiveness of convolutional neural
network (CNN) features as inputs. CNN features have non-negative values when
using the rectified linear unit as an activation function. This naturally leads
us to model a set of CNN features by a convex cone and measure the geometric
similarity of convex cones for classification. To establish this framework, we
sequentially define multiple angles between two convex cones by repeating the
alternating least squares method and then define the geometric similarity
between the cones using the obtained angles. Moreover, to enhance our method,
we introduce a discriminant space, maximizing the between-class variance (gaps)
and minimizes the within-class variance of the projected convex cones onto the
discriminant space, similar to a Fisher discriminant analysis. Finally,
classification is based on the similarity between projected convex cones. The
effectiveness of the proposed method was demonstrated experimentally using a
private, multi-view hand shape dataset and two public databases.
