Ocular phenotype related SNP analysis in Southern Han Chinese population from Guangdong province

Liang, Yimeng; Liu, Heming; Gao, Zhen-Jie; Li, Qi; Li, Guo‐Ran; Zhao, Jian; Wang, Xiaoguang

doi:10.1016/j.gene.2022.146458

Cited by 1 publication

(1 citation statement)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on Farkas's system, Qiao et al [11] define 26 soft tissue landmarks and use these landmarks to extract facial phenotype measurements to analyze the genetic and geographical associations. Liang et al [12] used the landmarks ex and en to extract the eye phenotype. In addition, Farkas landmarks are also widely used in the study of human face morphology [13,14], and there are also many variants and derivatives [15].…”

Section: Landmark Selection Strategymentioning

confidence: 99%

Automatic labeling of 3D facial acupoint landmarks

Yin,

Fang,

2024

Metaverse

View full text Add to dashboard Cite

As special marks on a human face, facial landmarks reflect the facial features of various parts of the face, which is crucial in biomedicine and medical imaging. In addition, facial landmarks are also important features in computer vision such as face detection, face recognition, facial pose estimation, and facial animation. In this paper, we construct a 3D facial acupoint annotated dataset by labeling 37 facial acupoints on 846 neutral face triangle mesh on the FaceScape dataset. Based on these annotated data, we use a feature template matching method to realize the automatic annotation of 37 acupoints on triangle meshes. We used 40 meshes as the training set to extract the geometric patterns of 3D acupoints and then measured the performance of the automatic labeling algorithm on 20 meshes and 806 meshes as the test sets. In the training process, we extract the tangent plane for each landmark, project the neighbor vertices of the landmark to the tangent plane, and construct the feature image with R × R resolution through the bounding box of the projected points. In the testing process, we use the pattern images extracted during training to find the average features and use them as a guide to optimize the predicted landmarks. The experimental results show that our automatic acupoint labeling method has achieved good results.

show abstract