Abstract
Abstract. When employing Convolutional Neural Networks (CNNs) for facial expression recognition, several challenges are often encountered, such as facial occlusions, the limited size of reliable expression datasets, and inadequate precision in recognition outcomes. This paper preprocesses the dataset to enhance its reliability. By leveraging data synthesis and augmentation techniques, it employs a method of randomly generating occlusion blocks to integrate and expand the dataset. Based on the ResNet-18 network, the model is optimized by incorporating an attention mechanism, thereby improving the network's precision and robustness in recognizing facial expressions.
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