Privileged Attribution Constrained Deep Networks for Facial Expression Recognition

Bonnard, Jules; Dapogny, Arnaud; Dhombres, Ferdinand; Bailly, Kévin

doi:10.1109/icpr56361.2022.9956496

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…Bonnard, et al [44] Loss of privilege attribution The proposed method achieved good recognition results on both the RAF-DB dataset and the AffectNet dataset.…”

Section: Yg Et Al [43] a Multi-region Attention Transition Frameworkmentioning

confidence: 99%

“…Pseudo-label generation module improved the multi-region facial expression integration performance in a semi-supervised manner. Bonnard, et al [44] proposed loss of privilege attribution, a method that directs the model's attention to the most prominent facial regions, such as eyes, mouth, and eyebrows. And the loss of privilege attribution method did not depend on the backbone architecture.…”

Section: Related Workmentioning

confidence: 99%

“…Bonnard, et al. [44] proposed loss of privilege attribution, a method that directs the model's attention to the most prominent facial regions, such as eyes, mouth, and eyebrows. And the loss of privilege attribution method did not depend on the backbone architecture.…”

Section: Related Workmentioning

confidence: 99%

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DLSANet: Facial expression recognition with double‐code LBP‐layer spatial‐attention network

Guo

Wang

et al. 2023

IET Image Processing

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Facial expression recognition (FER) is widely used in many fields. To further improve the accuracy of FER, this paper proposes a method based on double‐code LBP‐layer spatial‐attention network (DLSANet). The backbone model for the DLSANet is an emotion network (ENet), which is modified with a double‐code LBP (DLBP) layer and a spatial attention module. The DLBP layer is at the front of the first convolutional layer. More valuable features can be extracted by inputting the image processed by DLBP into convolutional layers. The JAFFE and CK+ datasets are used, which contain seven expressions: happiness, anger, disgust, neutral, fear, sadness, and surprise. The average of fivefold cross‐validation shows that DLSANet achieves a recognition accuracy of 93.81% and 98.68% on the JAFFE and CK+ datasets. The experiment reveals that the DLSANet can produce better classification results than state‐of‐the‐art methods.

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“…Bonnard, et al [44] Loss of privilege attribution The proposed method achieved good recognition results on both the RAF-DB dataset and the AffectNet dataset.…”

Section: Yg Et Al [43] a Multi-region Attention Transition Frameworkmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%