Over the past five years, methods based on deep features have taken over the computer vision field. While dramatic performance improvements have been achieved for tasks such as face detection and verification, these methods usually need large amounts of annotated data. In practice, not all computer vision tasks have access to large amounts of annotated data. Facial expression analysis is such a task. In this dissertation, we focus on facial expression recognition and editing problems with small

more »

... datasets. In addition, to cope with challenging conditions like pose and occlusion, we also study unaligned facial attribute detection and occluded expression recognition problems. This dissertation has been divided into four parts. In the first part, we present FaceNet2ExpNet, a novel idea to train a light-weight and high accuracy classification model for expression recognition with small datasets. We first propose a new distribution function to model the high-level neurons of the expression network. Based on this, a two-stage training algorithm is carefully designed. In the pre-training stage, we train the convolutional layers of the expression net, regularized by the face net; In the refining stage, we append fully-connected layers to the pre-trained convolutional layers and train the whole network jointly. Visualization shows that the model trained with our method captures improved high-level expression semantics. Evaluations on four public expression databases demonstrate that our method achieves better results than state-of-the-art. In the second part, we focus on robust facial expression recognition under occlusion and propose a landmark-guided attention branch to find and discard corrupted feature elements from recognition. An attention map is first generated to indicate if a specific facial part is occluded and guide our model to attend to the non-occluded regions. To further increase robustness, we propose a facial region branch to partition the feature maps into non-overlapping facial blocks and enforce each blo [...]