Improved convolutional neural network-based approach using hand-crafted features for facial expression recognition

Khanbebin, Saeed Najafi; Mehrdad, Vahid

doi:10.1007/s11042-022-14122-1

Cited by 3 publications

(1 citation statement)

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“…From Table 5, it is evident that our proposed BGA-Net outperforms the method proposed by Khanbebin and Mehrdad [39], which combines handcrafted features with convolutional neural networks for deep feature extraction, by 1.89% on the Fer2013 dataset. In comparison to the approach introduced by Chang et al [40], which trains multiple classifiers based on the complexity of test samples to address the complexity of facial expression recognition, our BGA-Net achieves similar recognition performance in an end-to-end manner.…”

Section: Comparison With State-of-the-art Methodsmentioning

confidence: 89%

Convolutional Neural Network–Bidirectional Gated Recurrent Unit Facial Expression Recognition Method Fused with Attention Mechanism

Tang,

Zhang,

Tian

2023

Applied Sciences

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The relationships among different subregions in facial images and their varying contributions to facial expression recognition indicate that using a fixed subregion weighting scheme would result in a substantial loss of valuable information. To address this issue, we propose a facial expression recognition network called BGA-Net, which combines bidirectional gated recurrent units (BiGRUs) with an attention mechanism. Firstly, a convolutional neural network (CNN) is employed to extract feature maps from facial images. Then, a sliding window cropping strategy is applied to divide the feature maps into multiple subregions. The BiGRUs are utilized to capture the dependencies among these subregions. Finally, an attention mechanism is employed to adaptively focus on the most discriminative regions. When evaluated on CK+, FER2013, and JAFFE datasets, our proposed method achieves promising results.

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Section: Comparison With State-of-the-art Methodsmentioning

confidence: 89%