TY - JOUR
T1 - DLSANet
T2 - Facial expression recognition with double-code LBP-layer spatial-attention network
AU - Guo, Xing
AU - Lu, Siyuan
AU - Wang, Shuihua
AU - Lu, Zhihai
AU - Zhang, Yudong
N1 - Publisher Copyright:
© 2023 The Authors. IET Image Processing published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
PY - 2023/7/20
Y1 - 2023/7/20
N2 - Facial expression recognition (FER) is widely used in many fields. To further improve the accuracy of FER, this paper proposes a method based on double-code LBP-layer spatial-attention network (DLSANet). The backbone model for the DLSANet is an emotion network (ENet), which is modified with a double-code LBP (DLBP) layer and a spatial attention module. The DLBP layer is at the front of the first convolutional layer. More valuable features can be extracted by inputting the image processed by DLBP into convolutional layers. The JAFFE and CK+ datasets are used, which contain seven expressions: happiness, anger, disgust, neutral, fear, sadness, and surprise. The average of fivefold cross-validation shows that DLSANet achieves a recognition accuracy of 93.81% and 98.68% on the JAFFE and CK+ datasets. The experiment reveals that the DLSANet can produce better classification results than state-of-the-art methods.
AB - Facial expression recognition (FER) is widely used in many fields. To further improve the accuracy of FER, this paper proposes a method based on double-code LBP-layer spatial-attention network (DLSANet). The backbone model for the DLSANet is an emotion network (ENet), which is modified with a double-code LBP (DLBP) layer and a spatial attention module. The DLBP layer is at the front of the first convolutional layer. More valuable features can be extracted by inputting the image processed by DLBP into convolutional layers. The JAFFE and CK+ datasets are used, which contain seven expressions: happiness, anger, disgust, neutral, fear, sadness, and surprise. The average of fivefold cross-validation shows that DLSANet achieves a recognition accuracy of 93.81% and 98.68% on the JAFFE and CK+ datasets. The experiment reveals that the DLSANet can produce better classification results than state-of-the-art methods.
KW - artificial intelligence
KW - belief networks
KW - convolutional neural network
KW - local binary pattern
KW - pattern recognition
KW - spatial attention module
UR - http://www.scopus.com/inward/record.url?scp=85153600659&partnerID=8YFLogxK
U2 - 10.1049/ipr2.12817
DO - 10.1049/ipr2.12817
M3 - Article
AN - SCOPUS:85153600659
SN - 1751-9659
VL - 17
SP - 2659
EP - 2672
JO - IET Image Processing
JF - IET Image Processing
IS - 9
ER -