FACIAL EMOTION RECOGNITION BASED ON SELECTIVE KERNEL NETWORK

Abstract

Existing deep learning methods for facial emotion recognition only focus on optimizing network structures, utilizing fixed receptive fields for different images, and relying on feature extraction based on a single scale of receptive fields. However, this approach fails to fully capture the most critical facial regions. To address this limitation, this paper presents a novel technique for facial emotion recognition that employs a selective kernel network. The proposed method introduces a dedicated module called the selective kernel network, which is trained using transfer learning. This module incorporates various components, such as a selective attention mechanism and channel-wise independent feature extraction and fusion. These components allow for the extraction of feature information from key facial regions. Unlike other methods, the selective convolutional kernel network extracts features with multiple scales of receptive fields and adapts to different spatial positions using a multilayer perceptron. This adaptability enhances useful features and suppresses noise. After extracting the features, they are combined, and the classification outcome is computed using the softmax function. Experimental results demonstrate that the suggested approach achieves an accuracy of 88.4 and 92.1% on the RAF-DB and KDEF datasets, respectively. These results confirm the efficacy of the proposed technique in comprehensively capturing the most crucial facial regions. Moreover, compared to alternative methods, this technique exhibits superior accuracy and enhanced resilience.