  Recently, the recognition task of spontaneous facial micro-expressions has
attracted much attention with its various real-world applications. Plenty of
handcrafted or learned features have been employed for a variety of classifiers
and achieved promising performances for recognizing micro-expressions. However,
the micro-expression recognition is still challenging due to the subtle
spatiotemporal changes of micro-expressions. To exploit the merits of deep
learning, we propose a novel deep recurrent convolutional networks based
micro-expression recognition approach, capturing the spatial-temporal
deformations of micro-expression sequence. Specifically, the proposed deep
model is constituted of several recurrent convolutional layers for extracting
visual features and a classificatory layer for recognition. It is optimized by
an end-to-end manner and obviates manual feature design. To handle sequential
data, we exploit two types of extending the connectivity of convolutional
networks across temporal domain, in which the spatiotemporal deformations are
modeled in views of facial appearance and geometry separately. Besides, to
overcome the shortcomings of limited and imbalanced training samples, temporal
data augmentation strategies as well as a balanced loss are jointly used for
our deep network. By performing the experiments on three spontaneous
micro-expression datasets, we verify the effectiveness of our proposed
micro-expression recognition approach compared to the state-of-the-art methods.
