  While several approaches to face emotion recognition task are proposed in
literature, none of them reports on power consumption nor inference time
required to run the system in an embedded environment. Without adequate
knowledge about these factors it is not clear whether we are actually able to
provide accurate face emotion recognition in the embedded environment or not,
and if not, how far we are from making it feasible and what are the biggest
bottlenecks we face.
  The main goal of this paper is to answer these questions and to convey the
message that instead of reporting only detection accuracy also power
consumption and inference time should be reported as real usability of the
proposed systems and their adoption in human computer interaction strongly
depends on it. In this paper, we identify the state-of-the art face emotion
recognition methods that are potentially suitable for embedded environment and
the most frequently used datasets for this task. Our study shows that most of
the performed experiments use datasets with posed expressions or in a
particular experimental setup with special conditions for image collection.
Since our goal is to evaluate the performance of the identified promising
methods in the realistic scenario, we collect a new dataset with
non-exaggerated emotions and we use it, in addition to the publicly available
datasets, for the evaluation of detection accuracy, power consumption and
inference time on three frequently used embedded devices with different
computational capabilities. Our results show that gray images are still more
suitable for embedded environment than color ones and that for most of the
analyzed systems either inference time or energy consumption or both are
limiting factor for their adoption in real-life embedded applications.
