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.

翻译：面部表情识别是机器学习中的关键组成部分，促进了多种应用的发展。然而，卷积神经网络（CNNs）常受灾难性遗忘问题的困扰，制约了其适应性。所提出的方法——情感中心生成式重放（emotion-centered generative replay, ECgr），通过整合生成对抗网络生成的合成图像来解决这一挑战。此外，ECgr 引入了一种质量保证算法，以确保生成图像的保真度。这种双重方法使卷积神经网络能够在学习新任务的同时保留先前知识，从而提升其在情感识别中的性能。在四个不同面部表情数据集上的实验结果表明，集成我们伪重放方法生成的图像，既能增强对目标数据集和源数据集的训练，又能使卷积神经网络保留先前学到的知识。