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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국정보처리학회 JIPS(Journal of Information Processing Systems) Journal of Information Processing Systems Vol.20 No.4
- 발행연도
- 2024.8
- 수록면
- 535 - 549 (15page)
- DOI
- 10.3745/JIPS.01.0107
이용수
초록· 키워드
오류제보하기
Facial expressions (FEs) serve as fundamental components for human emotion assessment and human–computer interaction. Traditional convolutional neural networks tend to overlook valuable information duringthe FE feature extraction, resulting in suboptimal recognition rates. To address this problem, we propose a deeplearning framework that incorporates hierarchical feature fusion, contextual data, and an attention mechanismfor precise FE recognition. In our approach, we leveraged an enhanced VGGNet16 as the backbone networkand introduced an improved group convolutional channel attention (GCCA) module in each block to emphasizethe crucial expression features. A partial decoder was added at the end of the backbone network to facilitate thefusion of multilevel features for a comprehensive feature map. A reverse attention mechanism guides the modelto refine details layer-by-layer while introducing contextual information and extracting richer expressionfeatures. To enhance feature distinguishability, we employed islanding loss in combination with softmax loss,creating a joint loss function. Using two open datasets, our experimental results demonstrated the effectivenessof our framework. Our framework achieved an average accuracy rate of 74.08% on the FER2013 dataset and98.66% on the CK+ dataset, outperforming advanced methods in both recognition accuracy and stability.
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