Facial Expression Recognition with Skip-Connection to Leverage Low-Level Features


Deep convolutional neural networks (CNNs) have established their feet in the ground of computer vision and machine learning, used in various applications. In this work, an attempt is made to learn a CNN for a task of facial expression recognition (FER). Our network has convolutional layers linked with an FC layer with a skip-connection to the classification layer. Motivation behind this design is that lower layers of a CNN are responsible for lower level features, and facial expressions can be mainly encoded in low-to-mid level features. Hence, in order to leverage the responses from lower layers, all convo-lutional layers are integrated via FC layers. Moreover, a network with shared parameters is used to extract landmark motion trajectory features. These visual and landmark features are fused to improve the performance. Our method is evaluated on the CK+ and Oulu-CASIA facial expression datasets.

Proc. IEEE International Conference on Image Processing