Deep Continual Learning for Emerging Emotion Recognition

Understanding an
unknown facial emotion
that emerges in the future underpins significant impacts in various domains. Knowing the fact that emotional states grow in vocabulary, new emotional states need to be adapted while the existing knowledge of known emotional states is preserved. While human beings spontaneously perform this task, the challenge is, how to devise a deep learning technique that can effectively recognize an unknown emotion category in the future. Although the deep convolutional neural network has shown excellent emotion recognition performances in the past, it is conventionally a predefined multi-way classifier showing little resilience towards adding a new emotion class. Considering the aforementioned challenge, in this paper, we propose a generic deep convolutional neural network-based architecture that constantly absorbs the upcoming emotion categories and recognizes them effectively. We further propose an indicator loss, which is associated with the distillation mechanism that preserves the existing knowledge. In order to demonstrate the feasibility of our proposed method, we evaluated our model using benchmark emotion datasets. The results confirm that the proposed approach is superior in recognizing unknown emotional states compared to continual learning benchmarks. Further, our proposed method demonstrates higher accuracy, compared to the transfer learning baselines.