Sphere Face Model: A 3D morphable model with hypersphere manifold latent space using joint 2D/3D training

Jiang, Diqiong; Jin, Yiwei; Zhang, Fang-Lue; Zhu, Zhe; Zhang, Yun; Tong, Ruofeng; Tang, Min

doi:10.1007/s41095-022-0286-4

Cited by 8 publications

(3 citation statements)

References 71 publications

(155 reference statements)

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“…D/G represents the defect degree of GDY. And there are also two distinctive characteristic peaks at the wavelengths of 1987.78 cm −1 and 2118.02 cm −1 , which are the vibrations of classical conjugated diacetylene bonds [18,35,36]. The occurrence of these feature peaks one more time proves that pure GDY has already successfully prepared.…”

Section: Xrd and Raman Analysismentioning

confidence: 99%

Graphdiyne (C_nH_2n−2)/NiWO₄ self-assembled p–n junction characterized with in situ XPS for efficient photocatalytic hydrogen production

Zhang,

Lei,

Jin

2023

2D Mater.

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As is well known, how to deeply understand the charge separation and charge transfer capabilities of catalysts, as well as how to optimize these capabilities of catalysts to improve hydrogen production performance, remains a huge challenge. In recent years, a new type of carbon material graphdiyne (GDY) has been proposed. GDY acetylene has a special atomic arrangement that graphene does not have a two-dimensional network of sp2 and sp conjugated intersections makes it easier to construct active sites and improve photocatalytic ability. In addition, GDY also has the advantage of adjusting the bandgap of other catalysts and inhibiting carrier recombination, making it more prone to hydrogen evolution reactions. In addition to using mechanical ball milling to produce GDY, NiWO4 without precious metals was also prepared. The sheet-like structure of GDY in the composite catalyst provides a anchoring site and more active sites for the granular NiWO4. And the composite catalyst fully enhances the good conductivity of GDY and its unique ability to enhance electron transfer, greatly improving the ability of NiWO4 as a single substance. Through in-situ x-ray photoelectron spectrometer, it was demonstrated that a p–n heterojunction was constructed between GDY and NiWO4 in the composite catalyst, further enhancing the synergistic effect between the two, resulting in a hydrogen production rate of 90.92 μmol for the composite catalyst is 4.56 times higher than that of GDY and 4.97 times higher than that of NiWO4, respectively, and the stability of the composite catalyst is significantly higher than that of each single catalyst.

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Section: Xrd and Raman Analysismentioning

confidence: 99%

Graphdiyne (C_nH_2n−2)/NiWO₄ self-assembled p–n junction characterized with in situ XPS for efficient photocatalytic hydrogen production

Zhang,

Lei,

Jin

2023

2D Mater.

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“…It is also the most common output generated by 3D scanners. It encompasses an unorganized collection of 3D coordinates corresponding to points on the facial surface [160]. In the past, it was viewed as a sparse approximation of the actual surface, but with the advent of point-based rendering and increases in storage and processing capabilities this perception is diminishing.…”

Section: B 3d Datamentioning

confidence: 99%

Multimodal Face Data Sets—A Survey of Technologies, Applications, and Contents

Al-Mannai,

Al-Thelaya,

Schneider

et al. 2024

IEEE Access

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Due to their ease-of-use, biometric verification methods to control access to digital devices have become ubiquitous. Many rely on supervised machine learning, a process that is notoriously datahungry. At the same time, biometric data is sensitive from a privacy perspective, and a comprehensive review from a data set perspective is lacking. In this survey, we present a comprehensive review of multimodal face data sets (e.g., data sets containing RGB color plus other channels such as infrared or depth). This follows a trend in both industry and academia to use such additional modalities to improve the robustness and reliability of the resulting biometric verification systems. Furthermore, such data sets open the path to a plethora of additional applications, such as 3D face reconstruction (e.g., to create avatars for VR and AR environments), face detection, registration, alignment, and recognition systems, emotion detection, anti-spoofing, etc. We also provide information regarding the data acquisition setup and data attributes (ethnicities, poses, facial expressions, age, population size, etc.) as well as a thorough discussion of related applications and state-of-the-art benchmarking. Readers may thus use this survey as a tool to navigate the existing data sets both from the application and data set perspective. To existing surveys we contribute, to the best of our knowledge, the first exhaustive review of multimodalities in these data sets.

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“…Extracting facial features is crucial for robust recognition, ensuring adaptability to environmental changes. This step is essential for enhancing recognition results [1].…”

Section: Introductionmentioning

confidence: 99%

Attendance System Optimization through Deep Learning Face Recognition

Ali,

Diwan,

Kumar

2024

IJCDS

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The significance of face recognition technology spans across diverse domains due to its practical applications. This study introduces an innovative face recognition system that seamlessly integrates Multi-task Cascaded Convolutional Neural Networks (MTCNN) for precise face detection, VGGFace for feature extraction, and Support Vector Machine (SVM) for efficient classification. The system demonstrates exceptional real-time performance in tracking multiple faces within a single frame, particularly excelling in attendance monitoring. Notably, the "VGGFace" model emerges as a standout performer, showcasing remarkable accuracy and achieving an impressive F-score of 95% when coupled with SVM. This underscores the model's effectiveness in recognizing facial identities, attributing its success to robust training on extensive datasets. The research underscores the potency of the VGGFace model, especially in collaboration with various classifiers, with SVM yielding notably high accuracy rates.

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Sphere Face Model: A 3D morphable model with hypersphere manifold latent space using joint 2D/3D training

Cited by 8 publications

References 71 publications

Graphdiyne (C_nH_2n−2)/NiWO₄ self-assembled p–n junction characterized with in situ XPS for efficient photocatalytic hydrogen production

Graphdiyne (C_nH_2n−2)/NiWO₄ self-assembled p–n junction characterized with in situ XPS for efficient photocatalytic hydrogen production

Multimodal Face Data Sets—A Survey of Technologies, Applications, and Contents

Attendance System Optimization through Deep Learning Face Recognition

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Sphere Face Model: A 3D morphable model with hypersphere manifold latent space using joint 2D/3D training

Cited by 8 publications

References 71 publications

Graphdiyne (CnH2n−2)/NiWO4 self-assembled p–n junction characterized with in situ XPS for efficient photocatalytic hydrogen production

Graphdiyne (CnH2n−2)/NiWO4 self-assembled p–n junction characterized with in situ XPS for efficient photocatalytic hydrogen production

Multimodal Face Data Sets—A Survey of Technologies, Applications, and Contents

Attendance System Optimization through Deep Learning Face Recognition

Contact Info

Product

Resources

About

Graphdiyne (C_nH_2n−2)/NiWO₄ self-assembled p–n junction characterized with in situ XPS for efficient photocatalytic hydrogen production

Graphdiyne (C_nH_2n−2)/NiWO₄ self-assembled p–n junction characterized with in situ XPS for efficient photocatalytic hydrogen production