Facial Expression Recognition with Skip-connection to Leverage Low-level Features

Manisha Verma, Hirokazu Kobori, Yuta Nakashima, Noriko Takemura, Hajime Nagahara

Abstract:

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 convolutional
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.