Abstract
Facial expression recognition is a pivotal component in machine learning, facilitating various applications. However, convolutional neural networks (CNNs) are often plagued by catastrophic forgetting, impeding their adaptability. The proposed method, emotion-centered generative replay (ECgr), tackles this challenge by integrating synthetic images from generative adversarial networks. Moreover, ECgr incorporates a quality assurance algorithm to ensure the fidelity of generated images. This dual approach enables CNNs to retain past knowledge while learning new tasks, enhancing their performance in emotion recognition. The experimental results on four diverse facial expression datasets demonstrate that incorporating images generated by our pseudo-rehearsal method enhances training on the targeted dataset and the source dataset while making the CNN retain previously learned knowledge.
Abstract (translated)
面部表情识别是机器学习的一个重要组成部分，促进了各种应用的发展。然而，卷积神经网络（CNNs）经常受到灾难性遗忘的困扰，这会阻碍其适应性。所提出的方法，情感为中心的生成性重放（ECgr），通过将生成对抗网络（GAN）生成的合成图像相结合来解决这一挑战。此外，ECgr 还包含一个质量保证算法，以确保生成图像的准确性。这种双方法使 CNN 能够保留过去的知识，同时学习新的任务，从而提高其在情感识别方面的性能。在四个多样的人脸表情数据集的实验结果中，采用我们伪重放方法生成的图像增强了目标数据集和源数据集的训练，同时使 CNN 保留之前学习的知识。
URL
https://arxiv.org/abs/2404.12260
