3D face recognition: A comprehensive survey in 2022

Computational Visual Media

Review Article

Shang Gao¹  Yaping Jing¹  Xuequan Lu¹ 

[1] The School of Information Technology, Deakin University, Waurn Ponds, VIC, Australia;

关键词: 3D face recognition;    3D face databases;    deep learning;    local features;    global features;   

DOI  :  10.1007/s41095-022-0317-1

 received in 2022-04-14, accepted in 2022-09-29,  发布年份 2022

来源: Springer

【 摘 要 】

In the past ten years, research on face recognition has shifted to using 3D facial surfaces, as 3D geometric information provides more discriminative features. This comprehensive survey reviews 3D face recognition techniques developed in the past decade, both conventional methods and deep learning methods. These methods are evaluated with detailed descriptions of selected representative works. Their advantages and disadvantages are summarized in terms of accuracy, complexity, and robustness to facial variations (expression, pose, occlusion, etc.). A review of 3D face databases is also provided, and a discussion of future research challenges and directions of the topic.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

Fig. 1