Since most existing facial expression recognition methods depend on deep learning models trained in isolation on a facial expression image corpora, once employed in scenarios that are different from those in the corpora, they usually demand ad-hoc retraining to be able to perform the expression recognition task again. Furthermore, most of these facial expression recognition methods are inconsistent when recognising person specific expressions or are incapable of adjusting to real-world scenarios where data is exclusively obtainable incrementally. In this paper, we present a face incremental expression recognition model, where we utilise domain incremental learning methods to learn individual facial features of facial expressions. We assume that each individual’s facial expression (domain) is presented to the model one domain at a time. We assessed our model’s ability to remember previously seen domains (individual’s facial expression) and incrementally perform on new face domains. Our model improves performance compared to a non-incremental learning model and an incremental learning model in facial expression recognition for individual data with different expression classes.
|Title of host publication||International Joint Conference on Neural Networks (IJCNN 2023)|
|Publication status||Published - 2 Aug 2023|