Abstract:
Unlike prevalent facial expressions, micro expressions
have subtle, involuntary muscle movements which are short-lived
in nature. These minute muscle movements reflect true emotions
of a person. Due to the short duration and low intensity, these
micro-expressions are very difficult to perceive and interpret
correctly. In this paper, we propose the dynamic representation of
micro-expressions to preserve facial movement information of a
video in a single frame. We also propose a Lateral Accretive
Hybrid Network (LEARNet) to capture micro-level features of an
expression in the facial region. The LEARNet refines the salient
expression features in accretive manner by incorporating
accretion layers (AL) in the network. The response of the AL holds
the hybrid feature maps generated by prior laterally connected
convolution layers. Moreover, LEARNet architecture
incorporates the cross decoupled relationship between convolution
layers which helps in preserving the tiny but influential facial
muscle change information. The visual responses of the proposed
LEARNet depict the effectiveness of the system by preserving both
high- and micro-level edge features of facial expression. The
effectiveness of the proposed LEARNet is evaluated on four
benchmark datasets: CASME-I, CASME-II, CAS(ME)^2 and
SMIC. The experimental results after investigation show a
significant improvement of 4.03%, 1.90%, 1.79% and 2.82% as
compared with ResNet on CASME-I, CASME-II, CAS(ME)^2
and SMIC datasets respectively.
