An SE(3) invariant description for 3D face recognition

Jribi, Majdi; Rihani, Amal; Khlifa, Ameni Ben; Ghorbel, Faouzi

doi:10.1016/j.imavis.2019.06.016

Cited by 5 publications

(3 citation statements)

References 32 publications

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“…There has been a wide variety of approaches on 3D static face identification [15,7,33,11]. In [15], an efficient method 3D face identification was presented.…”

Section: Related Workmentioning

confidence: 99%

“…This approach was applied on 3D face recognition and showed reduced computational time and storage requirements. Moreover, a 3D face description which is invariant under the Special Euclidean group SE(3) and independent to the original surface parameterization was proposed in [11].…”

Section: Related Workmentioning

confidence: 99%

“…To address this, 3D-based approaches were introduced [7,33,11]. By using richer geometrical features provided by 3D scanners, 3D-based face identification methods have shown robustness in cases appearance features are inconsistent (e.g.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Face-GCN: A Graph Convolutional Network for 3D Dynamic Face Identification/Recognition

Papadopoulos,

Kacem,

Shabayek

et al. 2021

Preprint

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Face identification/recognition has significantly advanced over the past years. However, most of the proposed approaches rely on static RGB frames and on neutral facial expressions. This has two disadvantages. First, important facial shape cues are ignored. Second, facial deformations due to expressions can have an impact on the performance of such a method. In this paper, we propose a novel framework for dynamic 3D face identification/recognition based on facial keypoints. Each dynamic sequence of facial expressions is represented as a spatio-temporal graph, which is constructed using 3D facial landmarks. Each graph node contains local shape and texture features that are extracted from its neighborhood. For the classification/identification of faces, a Spatio-temporal Graph Convolutional Network (ST-GCN) is used. Finally, we evaluate our approach on a challenging dynamic 3D facial expression dataset.

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“…There has been a wide variety of approaches on 3D static face identification [15,7,33,11]. In [15], an efficient method 3D face identification was presented.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%