  Classification of human emotions remains an important and challenging task
for many computer vision algorithms, especially in the era of humanoid robots
which coexist with humans in their everyday life. Currently proposed methods
for emotion recognition solve this task using multi-layered convolutional
networks that do not explicitly infer any facial features in the classification
phase. In this work, we postulate a fundamentally different approach to solve
emotion recognition task that relies on incorporating facial landmarks as a
part of the classification loss function. To that end, we extend a recently
proposed Deep Alignment Network (DAN) with a term related to facial features.
Thanks to this simple modification, our model called EmotionalDAN is able to
outperform state-of-the-art emotion classification methods on two challenging
benchmark dataset by up to 5%. Furthermore, we visualize image regions analyzed
by the network when making a decision and the results indicate that our
EmotionalDAN model is able to correctly identify facial landmarks responsible
for expressing the emotions.
