Partially Occluded Face Expression Recognition with CBAM-Based Residual Network for Teaching Scene
Küçük Resim Yok
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this paper, a deep residual network based on convolutional block attention module (CBAM) is proposed, which is utilized for feature extraction of partially occluded face expression data. The proposed method overcomes the problem of localized occlusion face feature extraction by focusing on the regions and channels containing important information in the occluded face data through CBAM. Multi-task cascaded convolutional networks (MTCNN) are firstly utilized to localize the key regions of face emotion, and then deep emotion features are extracted by CBAM-ResNet network. The final emotion labels are generated. The effectiveness of this paper's method is verified on the RAF-DB dataset and the occluded CK+ dataset. The experimental accuracy in the RAF-DB dataset is 76.3%, which is 3.74% and 1.64% higher than the accuracy produced by the method of RGBT, and the WLS-RF, respectively. Application experiments are carried out in the real teaching scenario, which verifies the applicability of the algorithm in the real teaching scene. © 2023 IEEE.
Açıklama
2023 China Automation Congress, CAC 2023 -- 17 November 2023 through 19 November 2023 -- -- 198194
Anahtar Kelimeler
Attention Mechanism, Cbam, Partially Occluded Facial Emotion Recognition, Teaching Scene
Kaynak
Proceedings - 2023 China Automation Congress, CAC 2023
WoS Q Değeri
Scopus Q Değeri
N/A
