PAtt-Lite: Lightweight Patch and Attention MobileNet for Challenging
  Facial Expression Recognition
Jia Le Ngwe Kian Ming Lim Chin Poo Lee Thian Song Ong
Facial Expression Recognition (FER) is a machine learning problem that deals
with recognizing human facial expressions. While existing work has achieved
performance improvements in recent years, FER in the wild and under challenging
conditions remains a challenge. In this paper, a lightweight patch and
attention network based on MobileNetV1, referred to as PAtt-Lite, is proposed
to improve FER performance under challenging conditions. A truncated
ImageNet-pre-trained MobileNetV1 is utilized as the backbone feature extractor
of the proposed method. In place of the truncated layers is a patch extraction
block that is proposed for extracting significant local facial features to
enhance the representation from MobileNetV1, especially under challenging
conditions. An attention classifier is also proposed to improve the learning of
these patched feature maps from the extremely lightweight feature extractor.
The experimental results on public benchmark databases proved the effectiveness
of the proposed method. PAtt-Lite achieved state-of-the-art results on CK+,
RAF-DB, FER2013, FERPlus, and the challenging conditions subsets for RAF-DB and
FERPlus. The source code for the proposed method will be available at
https://github.com/JLREx/PAtt-Lite.