  This article presents our results for the sixth Affective Behavior Analysis
in-the-wild (ABAW) competition. To improve the trustworthiness of facial
analysis, we study the possibility of using pre-trained deep models that
extract reliable emotional features without the need to fine-tune the neural
networks for a downstream task. In particular, we introduce several lightweight
models based on MobileViT, MobileFaceNet, EfficientNet, and DDAMFN
architectures trained in multi-task scenarios to recognize facial expressions,
valence, and arousal on static photos. These neural networks extract
frame-level features fed into a simple classifier, e.g., linear feed-forward
neural network, to predict emotion intensity, compound expressions, action
units, facial expressions, and valence/arousal. Experimental results for five
tasks from the sixth ABAW challenge demonstrate that our approach lets us
significantly improve quality metrics on validation sets compared to existing
non-ensemble techniques.
