  People can manipulate emotion expressions when interacting with others. For
example, acted anger can be expressed when stimuli is not genuinely angry with
an aim to manipulate the observer. In this paper, we aim to examine if the
veracity of anger can be recognized with observers' pupillary data with
computational approaches. We use Genetic-based Feature Selection (GFS) methods
to select time-series pupillary features of of observers who observe acted and
genuine anger of the video stimuli. We then use the selected features to train
a simple fully connected neural work and a two-stream neural network. Our
results show that the two-stream architecture is able to achieve a promising
recognition result with an accuracy of 93.58% when the pupillary responses from
both eyes are available. It also shows that genetic algorithm based feature
selection method can effectively improve the classification accuracy by 3.07%.
We hope our work could help daily research such as human machine interaction
and psychology studies that require emotion recognition .
