  In this paper, we propose a method for image-set classification based on
convex cone models. Image set classification aims to classify a set of images,
which were usually obtained from video frames or multi-view cameras, into a
target object. To accurately and stably classify a set, it is essential to
represent structural information of the set accurately. There are various
representative image features, such as histogram based features, HLAC, and
Convolutional Neural Network (CNN) features. We should note that most of them
have non-negativity and thus can be effectively represented by a convex cone.
This leads us to introduce the convex cone representation to image-set
classification. To establish a convex cone based framework, we mathematically
define multiple angles between two convex cones, and then define the geometric
similarity between the cones using the angles. Moreover, to enhance the
framework, we introduce a discriminant space that maximizes the between-class
variance (gaps) and minimizes the within-class variance of the projected convex
cones onto the discriminant space, similar to the Fisher discriminant analysis.
Finally, the classification is performed based on the similarity between
projected convex cones. The effectiveness of the proposed method is
demonstrated experimentally by using five databases: CMU PIE dataset, ETH-80,
CMU Motion of Body dataset, Youtube Celebrity dataset, and a private database
of multi-view hand shapes.
