3D Facial Expression Recognition Based on Geometric Feature Fusion

Main Article Content

Jinwei Wang, Quan Li


In order to overcome the problems of missing important information and di-mensionality disaster of three-dimensional (3D) facial expression, a novel method based  on geometric feature fusion is proposed. This approach initially extracts  distance feature vectors and angle feature vectors from key facial regions such as the eyes, mouth, and eyebrows using direct geometric feature. Subsequently, the K-nearest neighbor (K-NN) algorithm is used to obtain the distance feature vector candidate sets and angle feature vector candidate sets separately. Finally, the maximum-minimum rule is utilized to fuse these candidate sets into a single feature vector, completing the recognition of 3D facial expression. Experimental results on the BU-3DFE database demonstrate that this method achieves on overall recognition rate of 97.6%, exhibiting excellent robustness to varia-tions in facial expressions. Furthermore, this approach can serve as a valuable reference for future research in the field of 3D face recognition.

Article Details

Author Biography

Jinwei Wang, Quan Li

[1]Jinwei Wang

2,*Quan Li


[1] Liming Vocational University,  Quanzhou, Fujian, China

2 Hunan Automotive Engineering Vocational College, Zhuzhou,Hunan, China

*Corresponding author: Quan Li

Copyright © JES 2024 on-line : journal.esrgroups.org


H. Patil, A. Kothari,and K. Bhurchandi, 3-D face recognition: features, databases, algorithms and challenges, Artificial Intelligence Review,2015,44(3):393-441.

Luo Changwei, Yu Jun, YU Lingyun, Overview of research progress on 3-D face recognition, J Tsinghua Univ(Sci& Technol),2021,61(1):77-88.

W. Hariri, H. Tabia, N. Farah, A. Benouareth, and D. Declercq, 3D face recognition using covariance based descriptors, Pattern Recognition, 2016,78:1-7.

Guo Mengli, Da Feipeng, Deng Xing, Gai Shaoyan, 3D face recognition based on keypoints and local feature, Journal of Zhejiang University(Engineering Science),2017,(51)3:584-589.

M. Emambakhsh and A. Evans, Nasal patches and curves for expression-robust 3D face recognition, IEEE Trans. Pattern Anal. Mach. Intell,2017,39(5):995-1007.

Li Ye, Wang Yinghui, Liu Jing, Hao Wen, Expression-insensitive 3D face recognition by the fusion of multiple subject-specific curves, Neurocomputing ,2018, 275: 1295-1307.

Zhang Hongying, Yang Weimin, Wang Huisan, 3D face recognition combining local keypoints with isogeodesic curves, Laser Optoelectron,2020,22(57):1-8.

Li Xiaoli, Ruan Qiuqi, Ming Yue, 3D facial expression recognition based on basic geometric features, Proceedings of the IEEE International Conference on signal processing proceedings, 2010: 1366-1369.

Y. Ryan ,U.Uli,Application of the K-Nearest Neighbors (K-NN) Algorithmfor Classification of Heart Failure, Journal of Applied Intelligent System,2021,6(1):1-9.

A.H.S,Jones ,C. Hardiyanti ,Case Based Reasoning using K-Nearest Neighbor with Euclidean Distance for Early Diagnosis of Personality Disorder,International Journal of Information System & Technology,2021,5(1),23-30.

S.Vahidian, M. Morafah, WangWeijia, V.Kungurtsev, Chen Chen, M.Shah, and B. Lin.,Efficient distribution similarity identification in clustered federated learning via principal angles between client data subspaces. In Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023,pp,10043-10052.

Quan Wei, B. J. Matuszewski and L.-K. Shark, Statistical shape modelling for expression-invariant face analysis and recognition, Pattern Analysis and Applications, 2016, 19(3): 765-781.

W. Hariri, N. Farah and D. K. Vishwakarma, Deep and shallow covariance feature quantization for 3D facial expression recognition, Journal of Latex Class Files, arXiv preprint, arXiv: 2105.05708v1,2021.

Fu Yunfang, Deng Yujuan,Zhang Yuekui, Yang Zhengyan,Qi Ruili,Low rank tucker decomposition for 2D+3D facial expression recognition, Procedia Comput,2022 198,499-504.