Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China.
Sensors (Basel). 2024 Aug 21;24(16):5391. doi: 10.3390/s24165391.
In recent years, significant progress has been made in facial expression recognition methods. However, tasks related to facial expression recognition in real environments still require further research. This paper proposes a tri-cross-attention transformer with a multi-feature fusion network (TriCAFFNet) to improve facial expression recognition performance under challenging conditions. By combining LBP (Local Binary Pattern) features, HOG (Histogram of Oriented Gradients) features, landmark features, and CNN (convolutional neural network) features from facial images, the model is provided with a rich input to improve its ability to discern subtle differences between images. Additionally, tri-cross-attention blocks are designed to facilitate information exchange between different features, enabling mutual guidance among different features to capture salient attention. Extensive experiments on several widely used datasets show that our TriCAFFNet achieves the SOTA performance on RAF-DB with 92.17%, AffectNet (7 cls) with 67.40%, and AffectNet (8 cls) with 63.49%, respectively.
近年来，面部表情识别方法取得了重大进展。然而，真实环境中与面部表情识别相关的任务仍需要进一步研究。本文提出了一种具有多特征融合网络的三交叉注意力转换器（TriCAFFNet），以提高挑战性条件下的面部表情识别性能。通过结合面部图像的 LBP（局部二值模式）特征、HOG（方向梯度直方图）特征、地标特征和 CNN（卷积神经网络）特征，为模型提供了丰富的输入，提高了其辨别图像细微差异的能力。此外，设计了三交叉注意块，以促进不同特征之间的信息交换，使不同特征之间能够相互引导，捕捉显著的注意力。在几个广泛使用的数据集上进行的大量实验表明，我们的 TriCAFFNet 在 RAF-DB 上实现了 SOTA 性能，分别为 92.17%、AffectNet（7 cls）为 67.40%和 AffectNet（8 cls）为 63.49%。
