@Article{jai.2021.027069,
AUTHOR = {Jiancheng Zou, Jiaxin Li, Juncun Wei, Zhengzheng Li, Xin Yang},
TITLE = {Facial Expression Recognition Based on the Fusion of  Infrared and Visible Image},
JOURNAL = {Journal on Artificial Intelligence},
VOLUME = {3},
YEAR = {2021},
NUMBER = {3},
PAGES = {123--134},
URL = {http://www.techscience.com/jai/v3n3/46669},
ISSN = {2579-003X},
ABSTRACT = {Facial expression recognition is a research hot spot in the fields of 
computer vision and pattern recognition. However, the existing facial expression 
recognition models are mainly concentrated in the visible light environment. They 
have insufficient generalization ability and low recognition accuracy, and are 
vulnerable to environmental changes such as illumination and distance. In order 
to solve these problems, we combine the advantages of the infrared and visible 
images captured simultaneously by array equipment our developed with two 
infrared and two visible lens, so that the fused image not only has the texture 
information of visible image, but also has the contrast information of infrared 
image. On the other hand, we improved the WGAN by adding SSIM and LBP loss 
functions to ensure the structural similarity between the fused image and infrared 
image, and also the texture similarity between the fused image and visible image 
respectively. Finally, a facial expression recognition model Pyconv-SE18 with 
pyramid convolution and attention mechanism module is designed to extract the 
important feature information of facial expression in multiple scales. We add 
cosine distance loss function to reduce the feature difference within the class. 
Experiment results show that the robustness of expression recognition algorithm 
to illumination is improved based on the fused images. The accuracy of this model 
on FER2013 and CK+ public data sets are 69.3% and 94.6%, respectively.},
DOI = {10.32604/jai.2021.027069}
}