A Lightweight Attention-based Deep Network via Multi-Scale Feature
  Fusion for Multi-View Facial Expression Recognition
Ali Ezati Mohammadreza Dezyani Rajib Rana Roozbeh Rajabi Ahmad Ayatollahi
Convolutional neural networks (CNNs) and their variations have shown
effectiveness in facial expression recognition (FER). However, they face
challenges when dealing with high computational complexity and multi-view head
poses in real-world scenarios. We introduce a lightweight attentional network
incorporating multi-scale feature fusion (LANMSFF) to tackle these issues. For
the first challenge, we have carefully designed a lightweight fully
convolutional network (FCN). We address the second challenge by presenting two
novel components, namely mass attention (MassAtt) and point wise feature
selection (PWFS) blocks. The MassAtt block simultaneously generates channel and
spatial attention maps to recalibrate feature maps by emphasizing important
features while suppressing irrelevant ones. On the other hand, the PWFS block
employs a feature selection mechanism that discards less meaningful features
prior to the fusion process. This mechanism distinguishes it from previous
methods that directly fuse multi-scale features. Our proposed approach achieved
results comparable to state-of-the-art methods in terms of parameter counts and
robustness to pose variation, with accuracy rates of 90.77% on KDEF, 70.44% on
FER-2013, and 86.96% on FERPlus datasets. The code for LANMSFF is available at
https://github.com/AE-1129/LANMSFF.