  In this paper, the multi-task learning of lightweight convolutional neural
networks is studied for face identification and classification of facial
attributes (age, gender, ethnicity) trained on cropped faces without margins.
The necessity to fine-tune these networks to predict facial expressions is
highlighted. Several models are presented based on MobileNet, EfficientNet and
RexNet architectures. It was experimentally demonstrated that they lead to near
state-of-the-art results in age, gender and race recognition on the UTKFace
dataset and emotion classification on the AffectNet dataset. Moreover, it is
shown that the usage of the trained models as feature extractors of facial
regions in video frames leads to 4.5% higher accuracy than the previously known
state-of-the-art single models for the AFEW and the VGAF datasets from the
EmotiW challenges. The models and source code are publicly available at
https://github.com/HSE-asavchenko/face-emotion-recognition.
