  Facial micro-expressions are subtle and involuntary expressions that can
reveal concealed emotions. Micro-expressions are an invaluable source of
information in application domains such as lie detection, mental health,
sentiment analysis and more. One of the biggest challenges in this field of
research is the small amount of available spontaneous micro-expression data.
However, spontaneous data collection is burdened by time-consuming and
expensive annotation. Hence, methods are needed which can reduce the amount of
data that annotators have to review. This paper presents a novel
micro-expression spotting method using a recurrent neural network (RNN) on
optical flow features. We extract Histogram of Oriented Optical Flow (HOOF)
features to encode the temporal changes in selected face regions. Finally, the
RNN spots short intervals which are likely to contain occurrences of relevant
facial micro-movements. The proposed method is evaluated on the SAMM database.
Any chance of subject bias is eliminated by training the RNN using
Leave-One-Subject-Out cross-validation. Comparing the spotted intervals with
the labeled data shows that the method produced 1569 false positives while
obtaining a recall of 0.4654. The initial results show that the proposed method
would reduce the video length by a factor of 3.5, while still retaining almost
half of the relevant micro-movements. Lastly, as the model gets more data, it
becomes better at detecting intervals, which makes the proposed method suitable
for supporting the annotation process.
