3D Face Recognition using surface normals

Bagchi, Parama; Bhattacharjee, Debotosh; Nasipuri, Mita

doi:10.1109/tencon.2015.7372819

Cited by 11 publications

(3 citation statements)

References 6 publications

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“…This method is robust to the noise and occlusions. Later, they improved the registration method and proposed an across-pose method in [111]. [112] also proposed a 3D face recognition method with pose-invariant and a coarse-to-fine approach to detect landmarks under large yaw variations.…”

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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“…They derive the normal vectors from the Gabor wavelet filtered depth maps. Bagchi et al [34] extracted the significant depth values of the surface normals, then applying singular value decomposition stored them in the form of a feature vector. In [35], in order to improve the recognition accuracy, the normal surface vector and the principal curvature direction are used to obtain directional discrimination.…”

Section: Gradient‐based Feature Extractionmentioning

confidence: 99%

Use of gradient and normal vectors for face recognition

et al. 2020

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“…Three main problems in creating 3D face recognition systems that many researchers report are the 3D face pose, illumination changes, and variations in facial expression. Extracting better features are a key process for 3D face recognition (Bagchi, Bhattacharjee & Nasipuri, 2015;Zhang et al, 2016;Nagi et al, 2013;Wang et al, 2015;Zhu et al, 2017). Furthermore, shallow learning (such as machine learning) including only one or no layer of hidden units leads to lack of ability to deal with large-scale data.…”

Section: Introductionmentioning

confidence: 99%

3D texture-based face recognition system using fine-tuned deep residual networks

Zheng

Rahmat

Khalid

et al. 2019

PeerJ Computer Science

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As the technology for 3D photography has developed rapidly in recent years, an enormous amount of 3D images has been produced, one of the directions of research for which is face recognition. Improving the accuracy of a number of data is crucial in 3D face recognition problems. Traditional machine learning methods can be used to recognize 3D faces, but the face recognition rate has declined rapidly with the increasing number of 3D images. As a result, classifying large amounts of 3D image data is time-consuming, expensive, and inefficient. The deep learning methods have become the focus of attention in the 3D face recognition research. In our experiment, the end-to-end face recognition system based on 3D face texture is proposed, combining the geometric invariants, histogram of oriented gradients and the fine-tuned residual neural networks. The research shows that when the performance is evaluated by the FRGC-v2 dataset, as the fine-tuned ResNet deep neural network layers are increased, the best Top-1 accuracy is up to 98.26% and the Top-2 accuracy is 99.40%. The framework proposed costs less iterations than traditional methods. The analysis suggests that a large number of 3D face data by the proposed recognition framework could significantly improve recognition decisions in realistic 3D face scenarios.

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3D Face Recognition using surface normals

Cited by 11 publications

References 6 publications

3D Face Recognition: A Survey

3D Face Recognition: A Survey

Use of gradient and normal vectors for face recognition

3D texture-based face recognition system using fine-tuned deep residual networks

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