# Continual Learning for Affective Computing

arXiv: https://arxiv.org/abs/2006.06113 · HTML: https://mlsanity.com/paper/2006.06113/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2020.

## Built on by (most cited first, 1 of 1)

- [Continual Facial Expression Recognition: A Benchmark](https://mlsanity.com/paper/2305.06448/) (2023)

## Abstract (arXiv)

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.