  Automatically understanding and recognising human affective states using
images and computer vision can improve human-computer and human-robot
interaction. However, privacy has become an issue of great concern, as the
identities of people used to train affective models can be exposed in the
process. For instance, malicious individuals could exploit images from users
and assume their identities. In addition, affect recognition using images can
lead to discriminatory and algorithmic bias, as certain information such as
race, gender, and age could be assumed based on facial features. Possible
solutions to protect the privacy of users and avoid misuse of their identities
are to: (1) extract anonymised facial features, namely action units (AU) from a
database of images, discard the images and use AUs for processing and training,
and (2) federated learning (FL) i.e. process raw images in users' local
machines (local processing) and send the locally trained models to the main
processing machine for aggregation (central processing). In this paper, we
propose a two-level deep learning architecture for affect recognition that uses
AUs in level 1 and FL in level 2 to protect users' identities. The architecture
consists of recurrent neural networks to capture the temporal relationships
amongst the features and predict valence and arousal affective states. In our
experiments, we evaluate the performance of our privacy-preserving architecture
using different variations of recurrent neural networks on RECOLA, a
comprehensive multimodal affective database. Our results show state-of-the-art
performance of $0.426$ for valence and $0.401$ for arousal using the
Concordance Correlation Coefficient evaluation metric, demonstrating the
feasibility of developing models for affect recognition that are both accurate
and ensure privacy.
