In the recent year, state-of-the-art for facial microexpression recognition have been significantly advanced by deep neural networks. The robustness of deep learning has yielded promising performance beyond that of traditional handcrafted approaches. Most works in literature emphasized on increasing the depth of networks and employing highly complex objective functions to learn more features. In this paper, we design a Shallow Triple Stream Three-dimensional CNN (STSTNet) that is computationally light whilst capable of extracting discriminative high level features and details of micro-expressions. The network learns from three optical flow features (i.e., optical strain, horizontal and vertical optical flow fields) computed based on the onset and apex frames of each video. Our experimental results demonstrate the effectiveness of the proposed STSTNet, which obtained an unweighted average recall rate of 0.7605 and unweighted F1-score of 0.7353 on the composite database consisting of 442 samples from the SMIC, CASME II and SAMM databases.

This paper investigates the moderating effect of national cultural contexts on the relationship between social networks and opportunity recognition. Data obtained from Taiwan and the UnitedStates support the proposition that cultural contexts, specifically the individualism-collectivism dimension, moderate the relationship between tie strength, structural holes, and opportunity recognition. Results indicate that in the United States, tie strength is negatively associated with opportunity identification and structural holes are positively associated with opportunity identification; whereas in Taiwan we find the opposite. The results also show that the interaction effect between bridging ties and tie strength on opportunity recognition varies depending on the cultural context.

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