Point cloud based deep convolutional neural network for 3D face recognition

Bhople, Anagha R.; Shrivastava, Akhilesh Mohan; Prakash, Surya

doi:10.1007/s11042-020-09008-z

Cited by 22 publications

(9 citation statements)

References 42 publications

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“…The idea is therefore to efficiently store the information of the surface only, avoiding to use way larger 3D voxel-based volumes. Since this type of representation is often used in face and object recognition 26,27 , as well as in automatic car driving 28 , many of the algorithms developed in this field are invariant to translations and rotations, making them potentially valuable for protein analysis.…”

Section: Resultsmentioning

confidence: 99%

PyUUL provides an interface between biological structures and deep learning algorithms

et al. 2022

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Structural bioinformatics suffers from the lack of interfaces connecting biological structures and machine learning methods, making the application of modern neural network architectures impractical. This negatively affects the development of structure-based bioinformatics methods, causing a bottleneck in biological research. Here we present PyUUL (https://pyuul.readthedocs.io/), a library to translate biological structures into 3D tensors, allowing an out-of-the-box application of state-of-the-art deep learning algorithms. The library converts biological macromolecules to data structures typical of computer vision, such as voxels and point clouds, for which extensive machine learning research has been performed. Moreover, PyUUL allows an out-of-the box GPU and sparse calculation. Finally, we demonstrate how PyUUL can be used by researchers to address some typical bioinformatics problems, such as structure recognition and docking.

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Section: Resultsmentioning

confidence: 99%

PyUUL provides an interface between biological structures and deep learning algorithms

et al. 2022

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“…Bhople et al 28 introduced a point cloud-based architecture for 3D-FR using a CNN approach. Both feature extraction and optimization methods are used in the proposed architecture to enhance the performance and efficiency of the recognition process.…”

Section: Related Workmentioning

confidence: 99%

“…The surveys from Refs. 16 to 28 revealed that there are a number of obstacles to achieving high recognition accuracy and precision while simultaneously decreasing the time factor, FR, and error rate using the currently available approaches. For this reason, this research proposes a new 3D CV paradigm-based FFRR framework.…”

Section: Related Workmentioning

confidence: 99%

Framework for facial recognition and reconstruction for enhanced security and surveillance monitoring using 3D computer vision

Wang

Zhang

2023

J. Electron. Imag.

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Workflows can be optimized with the use of three-dimensional (3D) computer vision (CV) by monitoring processes from many angles. The potential for increased efficiency and cost savings is clear here. Input scenarios are always being monitored by security and surveillance systems to recognize and classify items, human faces, and moving things. The input reconstruction in these surveillance systems is problematic because of the mapping from three dimensions to two dimensions in the form of pixels. We present a new 3D CV paradigm-based framework for facial recognition and reconstruction. In this setup, deep neural networks are used to distinguish between mapped and unmapped pixels during a 2D to 3D or 3D to 2D transformation. To use correlation analysis to recognize human faces, it is necessary to first examine the input image for textural properties. The mapping procedure continues with the extraction of textural elements using dimensional contours. Then, continuous contours are used to do the 3D mapping and reduce the number of false positives. Instead, this framework isolates individual contours of moving objects or faces to increase the number of training iterations. Finally, missing pixels in the dimension conversion are filled to rebuild the human face utilizing discrete and continuous outlines. Hence, the suggested framework minimizes the false rate (10%), increases the false positive rate as (13.93%), and minimizes the error by (9.89%) while maximizing the recognition accuracy for (13.93%) and the precision is (13.21%).

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“…Bhople at al. [136] proposed a network based on the PointNet architecture. It directly uses point cloud as input and uses the Siamese network for similarity learning.…”

Section: B 3d Face Recognitionmentioning

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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Point cloud based deep convolutional neural network for 3D face recognition

Cited by 22 publications

References 42 publications

PyUUL provides an interface between biological structures and deep learning algorithms

PyUUL provides an interface between biological structures and deep learning algorithms

Framework for facial recognition and reconstruction for enhanced security and surveillance monitoring using 3D computer vision

3D Face Recognition: A Survey

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