Alleviating Catastrophic Forgetting in Facial Expression Recognition
  with Emotion-Centered Models
Israel A. Laurensi Alceu de Souza Britto Jr. Jean Paul Barddal Alessandro Lameiras Koerich
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.