Sparse ICP With Resampling and Denoising for 3D Face Verification

Yu, Yi; Da, Feipeng; Guo, Yifan

doi:10.1109/tifs.2018.2889255

Cited by 15 publications

(8 citation statements)

References 49 publications

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“…An extension to this work was described in [92]. Recently, Yu et al [93] recommended utilizing the ICP (Iterative closest point) with resampling and denoising (RDICP) method to register each face patch to achieve high registration accuracy. With rigid registration, all face patches can be used to recognize the face, significantly improving the accuracy as they are less sensitive to expression or occlusion.…”

Section: B Local Feature-based Methodsmentioning

confidence: 99%

3D Face Recognition: A Survey

Jing,

Lu,

Gao

2021

Preprint

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Face recognition is one of the most studied research topics in the community. In recent years, the research on face recognition has shifted to using 3D facial surfaces, as more discriminating features can be represented by the 3D geometric information. This survey focuses on reviewing the 3D face recognition techniques developed in the past ten years which are generally categorized into conventional methods and deep learning methods. The categorized techniques are evaluated using detailed descriptions of the representative works. The advantages and disadvantages of the techniques are summarized in terms of accuracy, complexity and robustness to face variation (expression, pose and occlusions, etc). The main contribution of this survey is that it comprehensively covers both conventional methods and deep learning methods on 3D face recognition. In addition, a review of available 3D face databases is provided, along with the discussion of future research challenges and directions.

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Section: B Local Feature-based Methodsmentioning

confidence: 99%

3D Face Recognition: A Survey

Jing,

Lu,

Gao

2021

Preprint

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“…FRGC v2.0 and Bosphorus databases are being used for experiments and the proposed approach significantly improves the identification accuracy as compared with other current existing methods. Yu et al 43 have proposed a rigid registration approach based on surface resampling and denoising, which reduces the influence of sampling difference and noise on registration residuals. Bosphorus and FRGC v2.0 databases are used for experiment and the proposed algorithm outperforms the state-of-the-art algorithms.…”

Section: Related Workmentioning

confidence: 99%

(Retracted) Robust face recognition using multimodal data and transfer learning

et al. 2022

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The Editor-in-Chief and the publisher have retracted this article, which was submitted as part of a guest-edited special section. An investigation uncovered evidence of systematic manipulation of the publication process, including compromised peer review. The Editor and publisher no longer have confidence in the results and conclusions of the article. AMS, SDC, SCB, and SP either did not respond directly or could not be reached.

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“…Most previous methods [3,10,15,50] works directly on the 3D space of facial scans, since full information of the raw data is kept on this original domain. An overview of 3D face recognition method is presented in [1,5,30].…”

Section: D Face Recognitionmentioning

confidence: 99%

Data-Free Point Cloud Network for 3D Face Recognition

Zhang,

Da,

2019

Preprint

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Point clouds-based Networks have achieved great attention in 3D object classification, segmentation and indoor scene semantic parsing. In terms of face recognition, 3D face recognition method which directly consume point clouds as input is still under study. Two main factors account for this: One is how to get discriminative face representations from 3D point clouds using deep network; the other is the lack of large 3D training dataset. To address these problems, a data-free 3D face recognition method is proposed only using synthesized unreal data from statistical 3D Morphable Model to train a deep point cloud network. To ease the inconsistent distribution between model data and real faces, different point sampling methods are used in train and test phase. In this paper, we propose a curvatureaware point sampling(CPS) strategy replacing the original furthest point sampling(FPS) to hierarchically downsample feature-sensitive points which are crucial to pass and aggregate features deeply. A PointNet++ like Network is used to extract face features directly from point clouds. The experimental results show that the network trained on generated data generalizes well for real 3D faces. Fine tuning on a small part of FRGCv2.0 and Bosphorus, which include real faces in different poses and expressions, further improves recognition accuracy. * Corresponding Author tremely rely on face alignment algorithm and feature descriptors, which limit the scalability as compared to the endto-end learning approaches.

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Sparse ICP With Resampling and Denoising for 3D Face Verification

Cited by 15 publications

References 49 publications

3D Face Recognition: A Survey

3D Face Recognition: A Survey

(Retracted) Robust face recognition using multimodal data and transfer learning

Data-Free Point Cloud Network for 3D Face Recognition

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