Journal
PATTERN RECOGNITION
Volume 112, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2020.107694
Keywords
Facial expression recognition; Weighted mask; Attention; Oriented gradient
Funding
- National Foundation of China (NSFC) [61872067, 61872068, 61720106004]
- Department Science & Technology of Sichuan Province [2019YFH0016, 2020YFG0288]
- 111 Projects [B17008]
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This research proposes an oriented attention pseudo-siamese network for accurate facial expression recognition, which utilizes both global and local facial information. A direction-dependent attention mechanism is established to address the limitation of insufficient utilization of local information.
Facial Expression Recognition (FER) is a challenging yet important research topic owing to its significance with respect to its academic and commercial potentials. In this work, we propose an oriented attention pseudo-siamese network that takes advantage of global and local facial information for high accurate FER. Our network consists of two branches, a maintenance branch that consisted of several convolutional blocks to take advantage of high-level semantic features, and an attention branch that possesses a UNet-like architecture to obtain local highlight information. Specifically, we first input the face image into the maintenance branch. For the attention branch, we calculate the correlation coefficient between a face and its sub-regions. Next, we construct a weighted mask by correlating the facial landmarks and the correlation coefficients. Then, the weighted mask is sent to the attention branch. Finally, the two branches are fused to output the classification results. As such, a direction-dependent attention mechanism is established to remedy the limitation of insufficient utilization of local information. With the help of our attention mechanism, our network not only grabs a global picture but can also concentrate on important local areas. Experiments are carried out on 4 leading facial expression datasets. Our method has achieved a very appealing performance compared to other state-of-the-art methods. (C) 2020 Elsevier Ltd. All rights reserved.
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