  Real-world application requires affect perception models to be sensitive to
individual differences in expression. As each user is different and expresses
differently, these models need to personalise towards each individual to
adequately capture their expressions and thus, model their affective state.
Despite high performance on benchmarks, current approaches fall short in such
adaptation. In this work, we propose the use of Continual Learning (CL) for
affective computing as a paradigm for developing personalised affect
perception.
