Expression Recognition

Existing methods for facial expression recognition (FER) lack the utilization of prior facial knowledge, primarily focusing on …

Although expression descriptions provide additional information about facial behaviors despite of different poses, and pose features …

Recent studies on occluded facial expression recognition typically required fully expression-annotated facial images for training. …

Pose-invariant facial expression recognition is quite challenging due to variations in facial appearance and self-occlusion caused by …

In order to address problems in micro-expression recognition, we propose a micro-expression recognition framework that leverages macroexpression as guidance. Since subjects in macro-expression and micro-expression databases are different, Expression-Identity Disentangle Network (EIDNet) is introduced as feature extractor to disentangle expression-related features for expression samples.

To tackle the challenges in occluded facial expression recognition, we propose a step-wise learning strategy including two types of complementary adversarial learning. In this way, the occluded classifier can learn effective information from large-scale unpaired non-occluded facial images.

Since collecting paired visible and thermal facial images is often difficult, requiring paired data during training prevents the usage of the many available unpaired visible and thermal images, and thus may degenerate the learning effect of the visible facial expression classifier. To address this, we propose an unpaired adversarial facial expression recognition method. We tackle the unbalanced quantity of visible and thermal images by utilizing thermal images as privileged information. We introduce adversarial learning on the feature-level and label-level spaces to cope with unpaired training data. Finally, we add a decoder network to preserve the inherent visible features.

Previous facial expression recognition methods either focus on pose variations or identity bias; there is no work that considers both at the same time. To this end, we propose a novel feature representation method that uses adversarial learning to overcome the challenges of both pose variations and identity bias.

we propose using non-occluded facial images as privileged information to assist the learning process of the occluded view. Specifically, two deep neural networks are first trained from occluded and non-occluded images respectively.