Optimal marker set assessment for motion capture of 3D mimic facial (14) movements

被引:18
|
作者
Dagnes, Nicole [1 ,2 ]
Marcolin, Federica [2 ]
Vezzetti, Enrico [2 ]
Sarhan, Francois-Regis [3 ]
Dakpe, Stephanie [3 ,4 ,5 ]
Marin, Frederic [1 ]
Nonis, Francesca [2 ]
Ben Mansour, Khalil [1 ]
机构
[1] Univ Technol Compiegne, Sorbonne Univ, CNRS, UMR 7338, Compiegne, France
[2] Politecn Torino, Dept Management & Prod Engn, Turin, Italy
[3] CHU Amiens, Serv Chirurg Maxillofaciale, Amiens, France
[4] Inst Faire Faces, Amiens, France
[5] Univ Picardie Jules Verne, EA 7516 Chimere, Amiens, France
关键词
3D face; Face analysis; Motion capture; Marker optimization; Feature extraction;
D O I
10.1016/j.jbiomech.2019.06.012
中图分类号
Q6 [生物物理学];
学科分类号
071011 ;
摘要
Nowadays, facial mimicry studies have acquired a great importance in the clinical domain and 3D motion capture systems are becoming valid tools for analysing facial muscles movements, thanks to the remarkable developments achieved in the 1990s. However, the face analysis domain suffers from a lack of valid motion capture protocol, due to the complexity of the human face. Indeed, a framework for defining the optimal marker set layout does not exist yet and, up to date, researchers still use their traditional facial point sets with manually allocated markers. Therefore, the study proposes an automatic approach to compute a minimum optimized marker layout to be exploited in facial motion capture, able to simplify the marker allocation without decreasing the significance level. Specifically, the algorithm identifies the optimal facial marker layouts selecting the subsets of linear distances among markers that allow to automatically recognizing with the highest performances, through a k-nearest neighbours classification technique, the acted facial movements. The marker layouts are extracted from them. Various validation and testing phases have demonstrated the accuracy, robustness and usefulness of the custom approach. (C) 2019 Elsevier Ltd. All rights reserved.
引用
收藏
页码:86 / 93
页数:8
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