Study on facial part extraction for face similarity evaluation of Japanese terracotta figurines (Haniwa) from 3D point clouds

Ryousuke Namioka; Tsutomu Kinoshita; Xin Lu; Akio Kimura; Kouichi Konno

doi:10.1117/12.2626123

30 April 2022 Study on facial part extraction for face similarity evaluation of Japanese terracotta figurines (Haniwa) from 3D point clouds

Ryousuke Namioka, Tsutomu Kinoshita, Xin Lu, Akio Kimura, Kouichi Konno

Author Affiliations +

Proceedings Volume 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022; 121772B (2022) https://doi.org/10.1117/12.2626123
Event: International Workshop on Advanced Imaging Technology 2022 (IWAIT 2022), 2022, Hong Kong, China

Abstract

Haniwa were made for rituals during the Kofun period and were buried with the dead as funerary objects. By analyzing and classifying haniwa, archaeologists are trying to reveal information about their origins and evaluate their artistic values. Specifically, they observe haniwa carefully and classify them based on their characteristics and archaeological knowledge. Since observation is a subjective evaluation, an objective evaluation method is necessary to ensure authenticity. For objective evaluation, analysis based on digital data is effective. For example, 3D point clouds, which are digital data, can be easily obtained by photographic measurement. In [1 ], a 3D mesh is generated from a measured point cloud, and the haniwa face is analyzed based on the mesh. However, generating a mesh from a point cloud is time-consuming. In this paper, to evaluate the similarity of Haniwa faces, we investigate a method to extract the parts of Haniwa faces, such as eyes, mouth, and nose, directly from 3D point clouds.

Citation Download Citation

Ryousuke Namioka, Tsutomu Kinoshita, Xin Lu, Akio Kimura, and Kouichi Konno "Study on facial part extraction for face similarity evaluation of Japanese terracotta figurines (Haniwa) from 3D point clouds", Proc. SPIE 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022, 121772B (30 April 2022); https://doi.org/10.1117/12.2626123

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