  Facial emotion recognition is an essential and important aspect of the field
of human-machine interaction. Past research on facial emotion recognition
focuses on the laboratory environment. However, it faces many challenges in
real-world conditions, i.e., illumination changes, large pose variations and
partial or full occlusions. Those challenges lead to different face areas with
different degrees of sharpness and completeness. Inspired by this fact, we
focus on the authenticity of predictions generated by different <emotion,
region> pairs. For example, if only the mouth areas are available and the
emotion classifier predicts happiness, then there is a question of how to judge
the authenticity of predictions. This problem can be converted into the
contribution of different face areas to different emotions. In this paper, we
divide the whole face into six areas: nose areas, mouth areas, eyes areas, nose
to mouth areas, nose to eyes areas and mouth to eyes areas. To obtain more
convincing results, our experiments are conducted on three different databases:
facial expression recognition + ( FER+), real-world affective faces database
(RAF-DB) and expression in-the-wild (ExpW) dataset. Through analysis of the
classification accuracy, the confusion matrix and the class activation map
(CAM), we can establish convincing results. To sum up, the contributions of
this paper lie in two areas: 1) We visualize concerned areas of human faces in
emotion recognition; 2) We analyze the contribution of different face areas to
different emotions in real-world conditions through experimental analysis. Our
findings can be combined with findings in psychology to promote the
understanding of emotional expressions.
