  In this paper, we examine the multi-task training of lightweight
convolutional neural networks for face identification and classification of
facial attributes (age, gender, ethnicity) trained on cropped faces without
margins. It is shown that it is still necessary to fine-tune these networks in
order to predict facial expressions. Several models are presented based on
MobileNet, EfficientNet and RexNet architectures. It was experimentally
demonstrated that our models are characterized by the state-of-the-art emotion
classification accuracy on AffectNet dataset and near state-of-the-art results
in age, gender and race recognition for UTKFace dataset. Moreover, it is shown
that the usage of our neural network as a feature extractor of facial regions
in video frames and concatenation of several statistical functions (mean, max,
etc.) leads to 4.5\% higher accuracy than the previously known state-of-the-art
single models for AFEW and VGAF datasets from the EmotiW challenges. The models
and source code are publicly available at
https://github.com/HSE-asavchenko/face-emotion-recognition.
