Subject Identification Across Large Expression Variations Using 3D Facial Landmarks

TL;DR

This paper introduces a method using 3D facial landmarks and machine learning models to identify subjects across large expression variations, demonstrating superior performance on multiple emotion-based face databases.

Contribution

It is the first to apply 3D facial landmarks for subject identification across large expressions and provides a baseline for BP4D+ database.

Findings

Outperforms state-of-the-art on BU-4DFE and BP4D databases.

First to investigate subject identification on BP4D+.

Uses 3D landmarks with SVM, RF, and LSTM models.

Abstract

Landmark localization is an important first step towards geometric based vision research including subject identification. Considering this, we propose to use 3D facial landmarks for the task of subject identification, over a range of expressed emotion. Landmarks are detected, using a Temporal Deformable Shape Model and used to train a Support Vector Machine (SVM), Random Forest (RF), and Long Short-term Memory (LSTM) neural network for subject identification. As we are interested in subject identification with large variations in expression, we conducted experiments on 3 emotion-based databases, namely the BU-4DFE, BP4D, and BP4D+ 3D/4D face databases. We show that our proposed method outperforms current state of the art methods for subject identification on BU-4DFE and BP4D. To the best of our knowledge, this is the first work to investigate subject identification on the BP4D+, resulting in a baseline for the community.