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3D face reconstruction from a Single Image Using a Combined PCA-LPP Method

Computers, Materials & Continua 2023年第3期74卷 6213-6227页

作者： Jee-Sic Hur Hyeong-Geun Lee Shinjin Kang Yeo Chan Yoon Soo Kyun Kim Department of Computer Engineering Jeju National UniversityJeju63243Korea School of Games Hongik UniversitySejong30016Korea Department of Artificial Intelligence Jeju National UniversityJeju63243Korea

In this paper, we proposed a combined PCA-LPP algorithm toimprove 3D face reconstruction performance. Principal component analysis(PCA) is commonly used to compress images and extract features. Onedisadvantage of PCA is local feature loss. To address this, various studies haveproposed combining a PCA-LPP-based algorithm with a locality preservingprojection (LPP). However, the existing PCA-LPP method is unsuitable for3D face reconstruction because it focuses on data classification and clustering.In the existing PCA-LPP, the adjacency graph, which primarily shows the connectionrelationships between data, is composed of the e-or k-nearest neighbortechniques. By contrast, in this study, complex and detailed parts, such aswrinkles around the eyes and mouth, can be reconstructed by composing thetopology of the 3D face model as an adjacency graph and extracting localfeatures from the connection relationship between the 3D model vertices.Experiments verified the effectiveness of the proposed method. When theproposed method was applied to the 3D face reconstruction evaluation set,a performance improvement of 10% to 20% was observed compared with theexisting PCA-based method.

关键词： Principal component analysis locality preserving project 3DMM face reconstruction face modeling

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Advancing Wound Filling Extraction on 3D faces:An Auto-Segmentation and Wound face Regeneration Approach

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 2197-2214页

作者： Duong Q.Nguyen Thinh D.Le Phuong D.Nguyen Nga T.K.Le H.Nguyen-Xuan Department of Mathematics and Statistics Quy Nhon UniversityQuy Nhon City55100Viet Nam Applied Research Institute for Science and Technology Quy Nhon UniversityQuy Nhon City55100Viet Nam CIRTECH Institute HUTECH UniversityHo Chi Minh City72308Viet Nam

Facial wound segmentation plays a crucial role in preoperative planning and optimizing patient outcomes in various medical applications.In this paper,we propose an efficient approach for automating 3D facial wound segmentation using a two-stream graph convolutional network.Our method leverages the Cir3D-FaIR dataset and addresses the challenge of data imbalance through extensive experimentation with different loss functions.To achieve accurate segmentation,we conducted thorough experiments and selected a high-performing model from the trainedmodels.The selectedmodel demonstrates exceptional segmentation performance for complex 3D facial wounds.Furthermore,based on the segmentation model,we propose an improved approach for extracting 3D facial wound fillers and compare it to the results of the previous study.Our method achieved a remarkable accuracy of 0.9999993% on the test suite,surpassing the performance of the previous method.From this result,we use 3D printing technology to illustrate the shape of the wound filling.The outcomes of this study have significant implications for physicians involved in preoperative planning and intervention design.By automating facial wound segmentation and improving the accuracy ofwound-filling extraction,our approach can assist in carefully assessing and optimizing interventions,leading to enhanced patient outcomes.Additionally,it contributes to advancing facial reconstruction techniques by utilizing machine learning and 3D bioprinting for printing skin tissue implants.Our source code is available at https://***/SIMOGroup/WoundFilling3D.

关键词： 3D printing technology face reconstruction 3D segmentation 3D printed model

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