Facial Expression Recognition Using Frequency Neural Network

被引:34
|
作者
Tang, Yan [1 ]
Zhang, Xingming [1 ]
Hu, Xiping [2 ]
Wang, Siqi [3 ]
Wang, Haoxiang [1 ]
机构
[1] South China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510006, Peoples R China
[2] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China
[3] Natl Univ Def Technol, Coll Comp, Changsha 410073, Peoples R China
基金
中国国家自然科学基金;
关键词
Frequency-domain analysis; Feature extraction; Discrete cosine transforms; Face recognition; Deep learning; Neural networks; Facial features; Facial expression recognition; frequency domain analysis; deep learning; 2-D DCT; IMAGES; FACE;
D O I
10.1109/TIP.2020.3037467
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Facial expression recognition has become a newly-emerging topic in recent decades, which has important value in the field of human-computer interaction. In this paper, we present a deep learning based approach, named frequency neural network (FreNet), for facial expression recognition. Different from convolutional neural network in spatial domain, FreNet inherits the advantages of processing image in frequency domain, such as efficient computation and spatial redundancy elimination. First, we propose the learnable multiplication kernel and construct multiple multiplication layers to learn features in frequency domain. Second, a summarization layer is proposed following multiplication layers to further yield high-level features. Third, based on the property of discrete cosine transform (DCT), we utilize multiplication layers and summarization layer to construct the Basic-FreNet, which can yield high-level features on the widely used DCT feature. Finally, to further achieve better performance on Basic-FreNet, we propose the Block-FreNet in which the weight-shared multiplication kernel is designed for feature learning and the block sub-sampling is designed for dimension reduction. The experimental results show that the Block-FreNet not only achieves superior performance, but also greatly reduces the computational cost. To our best knowledge, the proposed approach is the first attempt to fill in the blank of frequency based deep learning model for facial expression recognition.
引用
收藏
页码:444 / 457
页数:14
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