HDFD --- A High Deformation Facial Dynamics Benchmark for Evaluation of Non-Rigid Surface Registration and Classification

TL;DR

The paper introduces HDFD, a comprehensive 4D facial dynamic dataset with challenging non-rigid deformations, designed to evaluate and advance non-rigid surface registration and classification methods.

Contribution

It provides a novel, detailed benchmark dataset with diverse facial deformations and a methodology for evaluating recent registration techniques.

Findings

The dataset includes 4D funny faces with non-isometric deformation.

It features 4D visual-audio Welsh speech faces with expert-rated fluency.

Evaluation shows the dataset's effectiveness in benchmarking registration methods.

Abstract

Objects that undergo non-rigid deformation are common in the real world. A typical and challenging example is the human faces. While various techniques have been developed for deformable shape registration and classification, benchmarks with detailed labels and landmarks suitable for evaluating such techniques are still limited. In this paper, we present a novel facial dynamic dataset HDFD which addresses the gap of existing datasets, including 4D funny faces with substantial non-isometric deformation, and 4D visual-audio faces of spoken phrases in a minority language (Welsh). Both datasets are captured from 21 participants. The sequences are manually landmarked, with the spoken phrases further rated by a Welsh expert for level of fluency. These are useful for quantitative evaluation of both registration and classification tasks. We further develop a methodology to evaluate several recent non-rigid surface registration techniques, using our dynamic sequences as test cases. The study demonstrates the significance and usefulness of our new dataset --- a challenging benchmark dataset for future techniques.