  Many existing facial expression recognition (FER) systems encounter
substantial performance degradation when faced with variations in head pose.
Numerous frontalization methods have been proposed to enhance these systems'
performance under such conditions. However, they often introduce undesirable
deformations, rendering them less suitable for precise facial expression
analysis. In this paper, we present eMotion-GAN, a novel deep learning approach
designed for frontal view synthesis while preserving facial expressions within
the motion domain. Considering the motion induced by head variation as noise
and the motion induced by facial expression as the relevant information, our
model is trained to filter out the noisy motion in order to retain only the
motion related to facial expression. The filtered motion is then mapped onto a
neutral frontal face to generate the corresponding expressive frontal face. We
conducted extensive evaluations using several widely recognized dynamic FER
datasets, which encompass sequences exhibiting various degrees of head pose
variations in both intensity and orientation. Our results demonstrate the
effectiveness of our approach in significantly reducing the FER performance gap
between frontal and non-frontal faces. Specifically, we achieved a FER
improvement of up to +5\% for small pose variations and up to +20\% improvement
for larger pose variations. Code available at
\url{https://github.com/o-ikne/eMotion-GAN.git}.
