摘要
Deep learning-assisted facial expression recognition has been extensively investigated in sentiment analysis,human-computer interaction,and security surveillance.Generally,the recognition accuracy in previous reports requires high-quality images and powerful computational resources.In this work,we quantitatively investigate the impacts of frequency-domain filtering on spatial-domain facial expression recognition.Based on the Fer2013 dataset,we filter out 82.64% of high-frequency components,resulting in a decrease of 3.85% in recognition accuracy.Our findings well demonstrate the essential role of low-frequency components in facial expression recognition,which helps reduce the reliance on high-resolution images and improve the efficiency of neural networks.
基金
supported by the Project of Jiangsu Education Department(No.2023SJYB0677)
the Natural Science Foundation of Jiangsu Province(No.BK20240005)
the Fundamental Research Funds for the Central Universities(No.021314380268)
the Science Research Project of Nanjing University of Science and Technology Zi Jin College(No.2022ZXKX0401012)。
