A Topology Standardized 3D Facial Dataset with Emotion and Action Unit Diversity for East Asians

We present a high-resolution 3D facial dataset focused on East Asian participants, designed to provide consistent topology and expression diversity. The dataset includes 98 individuals (18-30 years) and captures neutral faces, six basic emotions, and nine Facial Action Coding System (FACS)-based Action Units (AUs). Each 3D mesh is acquired under a standardized protocol. Synchronized multi-view RGB images are available only for a subset of participants who provided additional consent. To ensure structural consistency across subjects and expressions, we developed a processing pipeline combining FLAME-based expression fitting with Edge-Constrained Non-rigid Iterative Closest Point (Edge-NICP). The resulting meshes share unified topology and vertex correspondence, enabling direct point-to-point comparisons across conditions. Rich annotations, including facial landmarks, verified AU labels, and vertex-level deformation fields, accompany the dataset. Together, these resources may facilitate research in computer graphics, human-computer interaction, and cross-modal studies that benefit from expression-rich, topology-standardized 3D face data.

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Publication Details

Journal
Scientific Data
Published
2026-03-24
DOI
https://doi.org/10.1038/s41597-026-07098-2
Primary Topic
Face recognition and analysis
Type
article
Field-Weighted Citation Impact
0.00
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article

A Topology Standardized 3D Facial Dataset with Emotion and Action Unit Diversity for East Asians

Scientific Data
Face recognition and analysis
article

A Topology Standardized 3D Facial Dataset with Emotion and Action Unit Diversity for East Asians

article en

Abstract

We present a high-resolution 3D facial dataset focused on East Asian participants, designed to provide consistent topology and expression diversity. The dataset includes 98 individuals (18-30 years) and captures neutral faces, six basic emotions, and nine Facial Action Coding System (FACS)-based Action Units (AUs). Each 3D mesh is acquired under a standardized protocol. Synchronized multi-view RGB images are available only for a subset of participants who provided additional consent. To ensure structural consistency across subjects and expressions, we developed a processing pipeline combining FLAME-based expression fitting with Edge-Constrained Non-rigid Iterative Closest Point (Edge-NICP). The resulting meshes share unified topology and vertex correspondence, enabling direct point-to-point comparisons across conditions. Rich annotations, including facial landmarks, verified AU labels, and vertex-level deformation fields, accompany the dataset. Together, these resources may facilitate research in computer graphics, human-computer interaction, and cross-modal studies that benefit from expression-rich, topology-standardized 3D face data.

Scientific Data
Beihang University (CN)
Openalex Percentile: Top 10%
Face recognition and analysis
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