3D Face Reconstruction from a Single Shaded Image Using Subspace Crossing Engine

Sucontphunt, Tanasai

doi:10.1007/978-3-319-12568-8_96

Cited by 2 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The shape from shading-based method (SFS) estimates the 3D facial face shape through a nonlinear partial differential equation, which indicates the relationship between the intensity of each 2D image pixel and normal reflectivity. Tanasai et al [18] recovered a 3D face model from a single shaded face image. The key to their work was to find a possible relationship between an image subspace and a 3D subspace.…”

Section: Previous Workmentioning

confidence: 99%

Three-Dimensional Face Reconstruction Using Multi-View-Based Bilinear Model

Tian

Liu

Wei

2019

Sensors

View full text Add to dashboard Cite

Face reconstruction is a popular topic in 3D vision system. However, traditional methods often depend on monocular cues, which contain few feature pixels and only use their location information while ignoring a lot of textural information. Furthermore, they are affected by the accuracy of the feature extraction method and occlusion. Here, we propose a novel facial reconstruction framework that accurately extracts the 3D shapes and poses of faces from images captured at multi-views. It extends the traditional method using the monocular bilinear model to the multi-view-based bilinear model by incorporating the feature prior constraint and the texture constraint, which are learned from multi-view images. The feature prior constraint is used as a shape prior to allowing us to estimate accurate 3D facial contours. Furthermore, the texture constraint extracts a high-precision 3D facial shape where traditional methods fail because of their limited number of feature points or the mostly texture-less and texture-repetitive nature of the input images. Meanwhile, it fully explores the implied 3D information of the multi-view images, which also enhances the robustness of the results. Additionally, the proposed method uses only two or more uncalibrated images with an arbitrary baseline, estimating calibration and shape simultaneously. A comparison with the state-of-the-art monocular bilinear model-based method shows that the proposed method has a significantly higher level of accuracy.

show abstract

Section: Previous Workmentioning

confidence: 99%

Three-Dimensional Face Reconstruction Using Multi-View-Based Bilinear Model

Tian

Liu

Wei

2019

Sensors

View full text Add to dashboard Cite

show abstract

“…Recently, 3D face reconstruction has been a major trend and challenging work in computer vision field, which provides comprehensive face geometry and invariant to illumination compared with 2D imaging. However, previous works [ 15 , 16 , 17 , 18 ] show that 3D face reconstruction approaches are mainly based on 2D images, for example, shape from motion (SFM) [ 19 ], shape from shading (SFS) [ 20 ], multi-view geometry [ 18 ], etc. In addition, these strategies may require complicated instrument establishing, which is time consuming, and the accuracy of reconstruction models are often affected by illumination variations.…”

Section: Introductionmentioning

confidence: 99%

3D Face Point Cloud Reconstruction and Recognition Using Depth Sensor

Wang

Peng

2021

Sensors

View full text Add to dashboard Cite

Facial recognition has attracted more and more attention since the rapid growth of artificial intelligence (AI) techniques in recent years. However, most of the related works about facial reconstruction and recognition are mainly based on big data collection and image deep learning related algorithms. The data driven based AI approaches inevitably increase the computational complexity of CPU and usually highly count on GPU capacity. One of the typical issues of RGB-based facial recognition is its applicability in low light or dark environments. To solve this problem, this paper presents an effective procedure for facial reconstruction as well as facial recognition via using a depth sensor. For each testing candidate, the depth camera acquires a multi-view of its 3D point clouds. The point cloud sets are stitched for 3D model reconstruction by using the iterative closest point (ICP). Then, a segmentation procedure is designed to separate the model set into a body part and head part. Based on the segmented 3D face point clouds, certain facial features are then extracted for recognition scoring. Taking a single shot from the depth sensor, the point cloud data is going to register with other 3D face models to determine which is the best candidate the data belongs to. By using the proposed feature-based 3D facial similarity score algorithm, which composes of normal, curvature, and registration similarities between different point clouds, the person can be labeled correctly even in a dark environment. The proposed method is suitable for smart devices such as smart phones and smart pads with tiny depth camera equipped. Experiments with real-world data show that the proposed method is able to reconstruct denser models and achieve point cloud-based 3D face recognition.

show abstract

3D Face Reconstruction from a Single Shaded Image Using Subspace Crossing Engine

Cited by 2 publications

References 12 publications

Three-Dimensional Face Reconstruction Using Multi-View-Based Bilinear Model

Three-Dimensional Face Reconstruction Using Multi-View-Based Bilinear Model

3D Face Point Cloud Reconstruction and Recognition Using Depth Sensor

Contact Info

Product

Resources

About