A Lightweight Attention-based Deep Network via Multi-Scale Feature Fusion for Multi-View Facial Expression Recognition

• URL: http://arxiv.org/abs/2403.14318v1
• Date: Thu, 21 Mar 2024 11:40:51 GMT
• Title: A Lightweight Attention-based Deep Network via Multi-Scale Feature Fusion for Multi-View Facial Expression Recognition
• Authors: Ali Ezati, Mohammadreza Dezyani, Rajib Rana, Roozbeh Rajabi, Ahmad Ayatollahi,
• Abstract summary: We introduce a lightweight attentional network incorporating multi-scale feature fusion (LANMSFF) to tackle these issues. We present two novel components, namely mass attention (MassAtt) and point wise feature selection (PWFS) blocks. Our proposed approach achieved results comparable to state-of-the-art methods in terms of parameter counts and robustness to pose variation.
• Score: 2.9581436761331017
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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