Relatively small data sets available for expression recognition research make the training of
deep networks very challenging. Although fine-tuning can partially alleviate the issue, the …

Over the past few years, Convolutional Neural Networks (CNNs) have shown promise on
facial expression recognition. However, the performance degrades dramatically under real …

Pain is an unpleasant feeling that has been shown to be an important factor for the recovery
of patients. Since this is costly in human resources and difficult to do objectively, there is the …

The video-based facial expression recognition aims to classify a given video into several
basic emotions. How to integrate facial features of individual frames is crucial for this task. In …

Current works of facial expression learning in video consume significant computational
resources to learn spatial channel feature representations and temporal relationships. To …

In this paper, we propose a compact frame-based facial expression recognition framework
for facial expression recognition which achieves very competitive performance with respect …

In this paper, we propose a novel end-to-end architecture termed Spatio-Temporal
Convolutional features with Nested LSTM (STC-NLSTM), which learns the muti-level …

As surrogate functions of L_0-norm, many nonconvex penalty functions have been proposed
to enhance the sparse vector recovery. It is easy to extend these nonconvex penalty …

Neural network models of machine learning have shown promising prospects for visual
tasks, such as facial emotion recognition (FER). However, the generalization of the model …

Human communication includes rich emotional content, thus the development of multimodal
emotion recognition plays an important role in communication between humans and …