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Recent deep learning based facial expression recognition (FER) methods are mostly driven by the availability of large amount of training data. However, availability of such data is not always possible for FER in the wild where the infeasibility of obtaining sufficient training samples for each emotion category. Therefore, in this paper, we introduce the few-shot learning to construct a deep learning model named Convolutional Relation Network (CRN) for FER in the wild, which is learnt by exploiting a feature similarity comparison among the sufficient samples of the emotion categories to identify new classes with few samples. Specifically, our method learns a metric space in which classification can be performed by computing distances to capitalize on powerful discriminative ability of deep expression features to generalize the predictive power of the network. To achieve this, the features are constrained to maximize the distance between the features of different classes and discover the commonality of the same classes. Extensive experiments on three challenging in-the-wild datasets demonstrate that the proposed model significantly outperforms state-of-the-art methods.
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