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Review Article | Open Access

3D face recognition: A comprehensive survey in 2022

The School of Information Technology, Deakin University, Waurn Ponds, VIC, Australia
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Abstract

In the past ten years, research on facerecognition 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.

Keywords

3D face recognition3D face databasesdeep learninglocal featuresglobal features

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Computational Visual Media
Volume 9 Issue 4,
December 2023
Pages 657-685
DOI: 10.1007/s41095-022-0317-1
Cite this article:
Jing Y, Lu X, Gao S. 3D face recognition: A comprehensive survey in 2022. Computational Visual Media, 2023, 9(4): 657-685. https://doi.org/10.1007/s41095-022-0317-1
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