Automatic Quantification of Facial Asymmetry using Facial Landmarks

TL;DR

This paper introduces an objective, automated method to quantify facial asymmetry using facial landmarks and motion analysis, aiding diagnosis and monitoring of facial paralysis.

Contribution

It presents a novel deep learning and optical flow-based approach to objectively measure facial asymmetry from video sequences.

Findings

Effective in quantifying asymmetry in synthetic facial sequences

Potential to assist clinical diagnosis and rehabilitation monitoring

Provides a standardized asymmetry score for frontal faces

Abstract

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.