  One-sided facial paralysis causes uneven movements of facial muscles on the
sides of the face. Physicians currently assess facial asymmetry in a subjective
manner based on their clinical experience. This paper proposes a novel method
to provide an objective and quantitative asymmetry score for frontal faces. Our
metric has the potential to help physicians for diagnosis as well as monitoring
the rehabilitation of patients with one-sided facial paralysis. A deep learning
based landmark detection technique is used to estimate style invariant facial
landmark points and dense optical flow is used to generate motion maps from a
short sequence of frames. Six face regions are considered corresponding to the
left and right parts of the forehead, eyes, and mouth. Motion is computed and
compared between the left and the right parts of each region of interest to
estimate the symmetry score. For testing, asymmetric sequences are
synthetically generated from a facial expression dataset. A score equation is
developed to quantify symmetry in both symmetric and asymmetric face sequences.
