Identity-Invariant Facial Landmark Frontalization For Facial Expression Analysis

Abstract

We propose a frontalization technique for 2D facial landmarks, designed to aid in the analysis of facial expressions. It employs a new normalization strategy aiming to minimize identity variations, by displacing groups of facial landmarks to standardized locations. The technique operates directly on 2D landmark coordinates, does not require additional feature extraction and as such is computationally light. It achieves considerable improvement over a reference approach, justifying its use as an efficient preprocessing step for facial expression analysis based on geometric features.