High-order local normal derivative pattern (LNDP) for 3D face recognition

Soltanpour, Sima; Wu, Q. M. Jonathan

doi:10.1109/icip.2017.8296795

Cited by 12 publications

(7 citation statements)

References 21 publications

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“…The classification performance of methods applied to the FRGC-v2 dataset seem superior to the seemingly impressive results of published studies utilizing different methods in Table 1 (Hu et al, 2017;Sharma & Shaik, 2016;Soltanpour & Jonathan Wu, 2017).…”

Section: Resultsmentioning

confidence: 66%

“…The customized parameters such as weights and biases in CNN network results in a very slow convergence of training, and thus greatly increasing the training time and the number of epochs (Hu et al, 2017;Sharma & Shaik, 2016). In addition, when the dimension increases with the increase of data volume, it will lead to a curse of dimensionality problems and cause a drop in the performance of the classifier (Soltanpour & Jonathan Wu, 2017). The Fine-tuning method speeds up the convergence and shortens the training period, thus adapting to 3D processing.…”

Section: Resultsmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 66%

Section: Resultsmentioning

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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show abstract

“…The method in [90] used the regional boundary sphere descriptor (RBSR), which reduced the computational cost and improved the classification accuracy. [91] proposed a local derivative mode (LDP) descriptor based on local derivative changes. It can capture more detailed information than LBP.…”

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 order to optimize deeper neural networks for image recognition, He et al [ 20 ] presented a residual learning framework to ease the training of networks. Based on local derivative pattern (LDP), Soltanpour et al [ 21 ] proposed a descriptor for 3D face recognition. Focusing on the intrinsic invariance to pose and illumination changes, Mu et al [ 22 ] designed a lightweight yet powerful CNN with low-quality data to achieve an efficient and accurate deep learning solution.…”

Section: Related Workmentioning

confidence: 99%

Point CNN:3D Face Recognition with Local Feature Descriptor and Feature Enhancement Mechanism

Wang,

Lei,

Qian

2023

Sensors

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Three-dimensional face recognition is an important part of the field of computer vision. Point clouds are widely used in the field of 3D vision due to the simple mathematical expression. However, the disorder of the points makes it difficult for them to have ordered indexes in convolutional neural networks. In addition, the point clouds lack detailed textures, which makes the facial features easily affected by expression or head pose changes. To solve the above problems, this paper constructs a new face recognition network, which mainly consists of two parts. The first part is a novel operator based on a local feature descriptor to realize the fine-grained features extraction and the permutation invariance of point clouds. The second part is a feature enhancement mechanism to enhance the discrimination of facial features. In order to verify the performance of our method, we conducted experiments on three public datasets: CASIA-3D, Bosphorus, and Lock3Dface. The results show that the accuracy of our method is improved by 0.7%, 0.4%, and 0.8% compared with the latest methods on these three datasets, respectively.

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High-order local normal derivative pattern (LNDP) for 3D face recognition

Cited by 12 publications

References 21 publications

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

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

3D Face Recognition: A Survey

Point CNN:3D Face Recognition with Local Feature Descriptor and Feature Enhancement Mechanism

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