HDFD --- A High Deformation Facial Dynamics Benchmark for Evaluation of Non-Rigid Surface Registration and Classification
This provides a challenging benchmark for researchers in computer vision and graphics working on deformable shape analysis, though it is incremental as it builds on existing dataset efforts.
The authors introduced HDFD, a facial dynamics dataset with 4D funny faces and spoken phrases in Welsh, captured from 21 participants and manually landmarked, to address the lack of benchmarks for evaluating non-rigid surface registration and classification techniques.
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.