3D face recognition: Multi-scale strategy based on geometric and local descriptors

Abbad, Abdelghafour; Abbad, Khalid; Tairi, Hamid

doi:10.1016/j.compeleceng.2017.08.017

Cited by 24 publications

(7 citation statements)

References 19 publications

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“…Geometric and local shape descriptors seem to be promising in face recognition systems that are robust to distortions caused by facial expressions. Abbad et al [ 44 ] proposed an adaptation of the wave kernel signature (WKS) for shape analysis of the face as a local descriptor to construct a hybrid method involving radial and level curves of the face and the local information in the WKS. Local binary pattern (LBP) showed good results in 2D face analysis, and it was adapted to extract texture features on 3D facial depth images; the so-extracted features were suitable to fed a support vector machine algorithm (SVM) for classification [ 45 ].…”

Section: Discussionmentioning

confidence: 99%

Three-Dimensional Evaluation of Soft Tissue Malar Modifications after Zygomatic Valgization Osteotomy via Geometrical Descriptors

Olivetti

Marcolin

Moos

et al. 2021

JPM

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Patients with severe facial deformities present serious dysfunctionalities along with an unsatisfactory aesthetic facial appearance. Several methods have been proposed to specifically plan the interventions on the patient’s needs, but none of these seem to achieve a sufficient level of accuracy in predicting the resulting facial appearance. In this context, a deep knowledge of what occurs in the face after bony movements in specific surgeries would give the possibility to develop more reliable systems. This study aims to propose a novel 3D approach for the evaluation of soft tissue zygomatic modifications after zygomatic osteotomy; geometrical descriptors usually involved in face analysis tasks, i.e., face recognition and facial expression recognition, are here applied to soft tissue malar region to detect changes in surface shape. As ground truth for zygomatic changes, a zygomatic openness angular measure is adopted. The results show a high sensibility of geometrical descriptors in detecting shape modification of the facial surface, outperforming the results obtained from the angular evaluation.

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Section: Discussionmentioning

confidence: 99%

Three-Dimensional Evaluation of Soft Tissue Malar Modifications after Zygomatic Valgization Osteotomy via Geometrical Descriptors

Olivetti

Marcolin

Moos

et al. 2021

JPM

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“…Compared with previous methods, this method has shown high-class separability. Recently, [79] presented a geometry and local shape descriptor based on the Wave Kernel Signature (WKS) [80] to overcome the distortions caused by face expressions.…”

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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“…In recent years, artificial intelligence (AI) technologies, which is mainly centered on robots and pattern recognition, have been greatly studied with the development in the data acquisition technology, computer processing capacity and the accumulation of big data [1]. As one of the fundamental but challenging issue in computer vision, robotic and remote sensing [2], [3], 3D surface matching by local feature descriptors has been widely used in various applications [4], for instance, 3D object registration [5], 3D model retrieval [6], [7], 3D object recognition [8], [9], and 3D model reconstruction [10], [11].…”

Section: Introductionmentioning

confidence: 99%

3D Surface Matching by a Voxel-Based Buffer-Weighted Binary Descriptor

Zhou

Jiang

2019

IEEE Access

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3D surface matching by local feature descriptors is a fundamental task in 3D object registration, recognition, and retrieval. In view of 2D projected descriptors' information compression and 3D directly voxelized descriptors' sensitivity to density variation and boundary, this paper first proposes a voxel-based buffer-weighted binary descriptor, named VBBD. After local surfaces of detected keypoints are voxelized, each voxel's buffer region is established, and the buffer-weighted Gaussian kernel density is calculated. If the current voxel's buffer-weighted density is larger than its local surface's average density, the voxel is labeled 1, otherwise, it is 0. The proposed descriptor has several merits: 1) direct acquisition of 3D information without projection, the neighbor information is less compressed. 2) voxels are labeled and binarized according to the buffer-weighted density, which improves the robustness to boundary effect, noise and density variation. Based on the VBBD, a global optimal surface matching method based on a Kuhn_Munkres (KM) algorithm is adopted. The strategy has several advantages: 1) by calculating the descriptors of large-scaled surfaces which are down sampled, the number of the point cloud is reduced, and meanwhile, large-scaled information is obtained; and 2) KM algorithm is adopted to find matching pairs to achieve final maximum weight sum of all matching pairs, which can efficiently avoid local optimum. The experimental results show that, compared with other state-of-the-art descriptors, the VBBD has better descriptiveness and robustness, and the surface matching strategy by the VBBD can achieve both high recall and precision.INDEX TERMS Voxel, Gaussian kernel density, binary descriptor, Kuhn_Munkres matching.

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3D face recognition: Multi-scale strategy based on geometric and local descriptors

Cited by 24 publications

References 19 publications

Three-Dimensional Evaluation of Soft Tissue Malar Modifications after Zygomatic Valgization Osteotomy via Geometrical Descriptors

Three-Dimensional Evaluation of Soft Tissue Malar Modifications after Zygomatic Valgization Osteotomy via Geometrical Descriptors

3D Face Recognition: A Survey

3D Surface Matching by a Voxel-Based Buffer-Weighted Binary Descriptor

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