Alleviating Catastrophic Forgetting in Facial Expression Recognition with Emotion-Centered Models
This addresses a specific problem of catastrophic forgetting for researchers and practitioners in facial expression recognition, but it is incremental as it builds on existing generative replay methods.
The paper tackles catastrophic forgetting in facial expression recognition by proposing emotion-centered generative replay (ECgr), which uses synthetic images and a quality assurance algorithm to help CNNs retain past knowledge, resulting in enhanced performance on four datasets.
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.