2022
DOI: 10.3390/fi14090258
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Facial Expression Recognition Using Dual Path Feature Fusion and Stacked Attention

Abstract: Facial Expression Recognition (FER) can achieve an understanding of the emotional changes of a specific target group. The relatively small dataset related to facial expression recognition and the lack of a high accuracy of expression recognition are both a challenge for researchers. In recent years, with the rapid development of computer technology, especially the great progress of deep learning, more and more convolutional neural networks have been developed for FER research. Most of the convolutional neural … Show more

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Cited by 3 publications
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“…However, mostly due to expressionindependent intra-class differences, many of the convolutional neural performances are not good enough when dealing with the problems of overfitting from too-small datasets and noise. In [2] the authors propose a Dual Path Stacked Attention Network (DPSAN) to better handle the above challenges. In a first step of the developed methodology, the features of key regions in faces are extracted using segmentation, and irrelevant regions are ignored, which effectively suppresses intra-class differences.…”
mentioning
confidence: 99%
“…However, mostly due to expressionindependent intra-class differences, many of the convolutional neural performances are not good enough when dealing with the problems of overfitting from too-small datasets and noise. In [2] the authors propose a Dual Path Stacked Attention Network (DPSAN) to better handle the above challenges. In a first step of the developed methodology, the features of key regions in faces are extracted using segmentation, and irrelevant regions are ignored, which effectively suppresses intra-class differences.…”
mentioning
confidence: 99%