Previous works on actor identification mainly focused on static
features based on face identification and costume detection,
without considering the abundant dynamic information contained
in videos. In this paper, we propose a novel method
to mine representative actions of each actor, and show the remarkable
power of such actions for actor identification task.
Videos are firstly divided into shots and represented by BoW
based on spatial-temporal features. Then we integrate the prototype
theory with SVM to rank the shots and obtain the representative
actions. Our method for actor identification combines
representative actions with actors’ appearance. We validate
the method on episodes of the TV series “The Big Bang
Theory”. The experimental results show that the representative
actions are consistent with human judgements and can
greatly improve the matching performance as complementary
to existing handcrafted static features for actor identification.