中国科学院自动化研究所机构知识库

Facial emotion recognition is an essential aspect in
human-machine interaction. In the real-world conditions, it faces
many challenges, i.e., illumination changes, large pose variations
and partial or full occlusions, which cause different facial areas
with different sharpness and completeness. Inspired by this fact,
we focus on facial expression recognition based on partial faces in
this paper. We compare contribution of seven facial areas of lowresolution images, including nose areas, mouse areas, eyes areas,
nose to mouse areas, nose to eyes areas, mouth to eyes areas and
the whole face areas. Through analysis on the confusion matrix
and the class activation map, we find that mouth regions contain
much emotional information compared with nose areas and eyes
areas. In the meantime, considering larger facial areas is helpful
to judge the expression more precisely. To sum up, contributions
of this paper are two-fold: (1) We reveal concerned areas of
human in emotion recognition. (2) We quantify the contribution
of different facial parts.