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Effective Attention Feature Reconstruction Loss for Facial Expression Recognition in the Wild by Weijun Gong; Yingying Fan; Yurong Qian is a Computer Science article available to read on EtoBox.

What is Effective Attention Feature Reconstruction Loss for Facial Expression Recognition in the Wild about?

Facial expression recognition (FER) in the wild is very challenging due to occlusion, posture, illumination, and other uncontrolled factors. Learning discriminant features for FER using Convolutional Neural Networks is a momentous task for the significant class imbalance, wrong labels, inter-class similarities, and intra-class variations. The traditional method utilizes the Cross entropy loss function to optimize the convolutional network to obtain discriminative features for classification. However, this loss function cannot effectively solve the above problems in practice and cannot contribute to obtaining highly discriminant facial features for further analysis. Center loss improves the learning efficiency by reducing the intra-class distance of similar expressions, while the improvement of inter-class similarity, class imbalance, and generalization is insufficient. In this paper, we propose a lightweight Effective Attention Feature Reconstruction loss (EAFR loss), which can further optimize the feature space and enhance the discriminability of expression. The loss model is composed of the Focal Smoothing loss (FS loss) and the Aggregation-Separation loss (AS loss). Firstly, the

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Author

Weijun Gong; Yingying Fan; Yurong Qian

Publisher

Springer Science and Business Media LLC

Published

2022

Language

EN

Field

Computer Science (Physical Sciences)