Abstract
To solve the problem that facial expression recognition (FER) system in actual application scenariosis always interfered by complex background which lead to low accuracy, we designed a multi-scale local feature fusion network (MSLFnet) to improve the performance of FER in actual application scenarios. Middle-level facial features map are extracted from the backbone, and the middle-level local feature is generated by a patch-level local attention module, the network can obtain richer facial expressions. Experiments is carried out on the FER datasets RAF-DB and FER+ to verify the efficacy of the network. Experimental results show that the accuracy of the proposed network on RAF-DB and FER+ is 2.5% and 1% higher than original ResNet-18, proving the effectiveness of MSLFnet.
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