  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), that achieves state-of-the-art results
in the recent facial landmark recognition challenge, 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%.
