TY - JOUR
T1 - 3D-guided facial shape clustering and analysis
AU - Zhang, Jie
AU - Zhou, Kangneng
AU - Luximon, Yan
AU - Li, Ping
AU - Iftikhar, Hassan
N1 - Funding Information:
This work was financially supported by the Research Grants Council (RGC) of Hong Kong to conduct General Research Fund (GRF) (15603419).
Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2022/3
Y1 - 2022/3
N2 - Facial shape classification is of crucial importance in facial characteristics analysis and product recommendation. In this paper, we develop a 3D-guided facial shape clustering and analysis method to classify facial shapes without supervision, which is more reliable and accurate. This method consists of four steps: 3D face reconstruction, facial shape normalization, facial feature extraction and facial contour clustering. Firstly, we incorporate two 3D face reconstruction methods to reconstruct 3D face mesh without expression component from 1997 male and 2493 female facial images. Secondly, we normalize these 3D facial contours by translation and scaling. Thirdly, we propose two facial contour representations: geometric and anthropometric features. Fourthly, we use and compare three clustering methods to cluster these facial contours based on the extracted contour features by using Silhouette Coefficient and Calinski-Harabasz Index. The Circular Dendrogram of the hierarchical clustering result based on geometric features shows the optimal cluster number is 6 for 3D female and male faces and the analysis results demonstrate the K-means clustering on geometric features can achieve better performance. A further investigation between the beauty distribution and facial shape clusters reveals that the facial shapes with more pointed chin have higher beauty ratings, regardless of male or female. The facial shape analysis results can be applied in face-related product design, hairstyle recommendation and cartoon character creation. The code will be released to the public for research purpose: https://github.com/Easy-Shu/facial_shape_clustering
AB - Facial shape classification is of crucial importance in facial characteristics analysis and product recommendation. In this paper, we develop a 3D-guided facial shape clustering and analysis method to classify facial shapes without supervision, which is more reliable and accurate. This method consists of four steps: 3D face reconstruction, facial shape normalization, facial feature extraction and facial contour clustering. Firstly, we incorporate two 3D face reconstruction methods to reconstruct 3D face mesh without expression component from 1997 male and 2493 female facial images. Secondly, we normalize these 3D facial contours by translation and scaling. Thirdly, we propose two facial contour representations: geometric and anthropometric features. Fourthly, we use and compare three clustering methods to cluster these facial contours based on the extracted contour features by using Silhouette Coefficient and Calinski-Harabasz Index. The Circular Dendrogram of the hierarchical clustering result based on geometric features shows the optimal cluster number is 6 for 3D female and male faces and the analysis results demonstrate the K-means clustering on geometric features can achieve better performance. A further investigation between the beauty distribution and facial shape clusters reveals that the facial shapes with more pointed chin have higher beauty ratings, regardless of male or female. The facial shape analysis results can be applied in face-related product design, hairstyle recommendation and cartoon character creation. The code will be released to the public for research purpose: https://github.com/Easy-Shu/facial_shape_clustering
KW - 3D face reconstruction
KW - Facial beauty analysis
KW - Facial shape analysis
KW - Facial shape clustering
UR - http://www.scopus.com/inward/record.url?scp=85124287158&partnerID=8YFLogxK
U2 - 10.1007/s11042-022-12190-x
DO - 10.1007/s11042-022-12190-x
M3 - Journal article
AN - SCOPUS:85124287158
SN - 1380-7501
VL - 81
SP - 8785
EP - 8806
JO - Multimedia Tools and Applications
JF - Multimedia Tools and Applications
IS - 6
ER -