CAS-AIR-3D Face: A Low-Quality, Multi-Modal and Multi-Pose 3D Face Database

2021 ◽  
Author(s):  
Qi Li ◽  
Xiaoxiao Dong ◽  
Weining Wang ◽  
Caifeng Shan
Keyword(s):  
Face Database ◽  
3D Face ◽  
3D Face Database

3D Face Recognition Based on Regional Shape Maps

2018 ◽  
Vol 22 (1) ◽  
pp. 141-146 ◽  
Author(s):  
Xiaoni Wang ◽  
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
3D Face Recognition ◽  
Face Database ◽  
Registration Method ◽  
3D Face ◽  
Feature Vectors ◽  
Input Face ◽  
Point To Point ◽  
3D Face Database

This study proposes an iterative closest shape point (ICSP) registration method based on regional shape maps for 3D face recognition. A neutral expression image randomly selected from a face database is considered as the reference face. The point-to-point correspondences between the input face and the reference face are achieved by constructing the points’ regional shape maps. The distance between corresponding point pairs is then minimized by iterating through the correspondence findings and coordinate transformations. The vectors composed of the closest shape points obtained in the last iteration are regarded as the feature vectors of the input face. These 3D face feature vectors are finally used for both training and recognition using the Fisherface method. Experiments are conducted using the 3D face database maintained by the Chinese Academy of Science Institute of Automation (CASIA). The results show that the proposed method can effectively improve 3D face recognition performance.

Quasi-Facial Communication for Online Learning Using 3D Modeling Techniques

2010 ◽  
pp. 181-191
Author(s):  
Wang Yushun ◽  
Zhuang Yueting
Keyword(s):  
Online Learning ◽  
Reference Model ◽  
Texture Mapping ◽  
Feature Points ◽  
Face Database ◽  
3D Face ◽  
Distant Learning ◽  
Facial Modeling ◽  
The Individual ◽  
3D Face Database

Online interaction with 3D facial animation is an alternative way of face-to-face communication for distance education. 3D facial modeling is essential for virtual educational environments establishment. This article presents a novel 3D facial modeling solution that facilitates quasi-facial communication for online learning. Our algorithm builds 3D facial models from a single image, with support of a 3D face database. First from the image, we extract a set of feature points, which are then used to automatically estimate the head pose parameters using the 3D mean face in our database as a reference model. After the pose recovery, a similarity measurement function is proposed to locate the neighborhood for the given image in the 3D face database. The scope of neighborhood can be determined adaptively using our cross-validation algorithm. Furthermore, the individual 3D shape is synthesized by neighborhood interpolation. Texture mapping is achieved based on feature points. The experimental results show that our algorithm can robustly produce 3D facial models from images captured in various scenarios to enhance the lifelikeness in distant learning.

Building Large Scale 3D Face Database for Face Analysis

2007 ◽  
pp. 343-350 ◽  
Author(s):  
Yuxiao Hu ◽  
Zhenqiu Zhang ◽  
Xun Xu ◽  
Yun Fu ◽  
Thomas S. Huang
Keyword(s):  
Large Scale ◽  
Face Database ◽  
Face Analysis ◽  
3D Face ◽  
3D Face Database

Quasi-Facial Communication for Online Learning Using 3D Modeling Techniques

2010 ◽  
pp. 727-737
Author(s):  
Yushun Wang ◽  
Yueting Zhuang
Keyword(s):  
Online Learning ◽  
Reference Model ◽  
Texture Mapping ◽  
Feature Points ◽  
Face Database ◽  
3D Face ◽  
Distant Learning ◽  
Facial Modeling ◽  
The Individual ◽  
3D Face Database

Online interaction with 3D facial animation is an alternative way of face-to-face communication for distance education. 3D facial modeling is essential for virtual educational environments establishment. This article presents a novel 3D facial modeling solution that facilitates quasi-facialcommunication for online learning. Our algorithm builds 3D facial models from a single image, with support of a 3D face database. First from the image, we extract a set of feature points, which are then used to automatically estimate the head pose parameters using the 3D mean face in our database as a reference model. After the pose recovery, a similarity measurement function is proposed to locate the neighborhood for the given image in the 3D face database. The scope of neighborhood can be determined adaptivelyusing our cross-validation algorithm. Furthermore, the individual 3D shape is synthesized by neighborhood interpolation. Texture mapping is achieved based on feature points. The experimental results show that our algorithm can robustly produce 3D facial models from images captured in various scenarios to enhance the lifelikeness in distant learning.

scholarly journals Introducing the FIDENTIS 3D Face Database

2018 ◽  
Vol 81 (2) ◽  
pp. 202-223 ◽  
Author(s):  
Petra Urbanová ◽  
Zuzana Ferková ◽  
Marie Jandová ◽  
Mikoláš Jurda ◽  
Dominik Černý ◽  
...  
Keyword(s):  
Large Scale ◽  
Applied Research ◽  
Face Database ◽  
Web Based ◽  
3D Face ◽  
3D Data ◽  
Varied Composition ◽  
3D Scans ◽  
Research Domains ◽  
3D Face Database

Abstract Face databases have assumed an important role in a variety of clinical and applied research domains. However, the number of datasets accessible to the scientific community is limited and the knowledge of their existence may be concealed from a wider range of specialists. In the present paper we introduce a sizeable dataset of 3D facial scans - FIDENTIS 3D Face Database (F3D-FD or FIDENTIS Database), which is accompanied by basic demographic and descriptive data. The database is structured according to recorded subjects, and comprises single-scan entries as well as a smaller number of multiscan entries. The multi-scan entries vary in the time passed between recording sessions and in the devices employed to collect the 3D data. The total number of 2476 individuals puts our database within the category of large-scale databases. The 3D scans are accessible through a web-based interface at www. fidentis.cz. A licensed version of the database is available to interested parties upon signing a license agreement. Because of its varied composition, and low target-specificity the database has capacity to be of great assistance for the worldwide research community.

Building a Standard 3D Face Database Based on Planar Templates

2011 ◽  
Vol 48-49 ◽  
pp. 280-283
Author(s):  
Xin Xin Li ◽  
Xun Gong
Keyword(s):  
3D Models ◽  
Face Database ◽  
Matching Method ◽  
Matching Accuracy ◽  
Point Matching ◽  
Dense Point ◽  
The Mean ◽  
Point To Point ◽  
3D Face Database ◽  
2D Space

This paper presents a new point matching method to solve the dense point-to-point alignment of scanned 3D faces. Texture maps of 3D models are generated at first by unwrapping 3D faces to 2D space. Then, we build planar templates based on the mean shape computed by a group of annotated texture map. 34 landmarks on the unwrapped texture images are located by AAM and the final correspondence is built according to the templates. Comparing to the traditional algorithms, the presented point matching method can achieve good matching accuracy and stability.

2D face database diversification based on 3D face modeling

2011 ◽  
Author(s):  
Qun Wang ◽  
Jiang Li ◽  
Vijayan K. Asari ◽  
Mohammad A. Karim
Keyword(s):  
Face Database ◽  
Face Modeling ◽  
3D Face ◽  
3D Face Modeling

NONITERATIVE 3D FACE RECONSTRUCTION BASED ON PHOTOMETRIC STEREO

2008 ◽  
Vol 22 (03) ◽  
pp. 389-410 ◽  
Author(s):  
SANG-WOONG LEE ◽  
PATRICK S. P. WANG ◽  
SVETLANA N. YANUSHKEVICH ◽  
SEONG-WHAN LEE
Keyword(s):  
Photometric Stereo ◽  
Face Database ◽  
3D Face Reconstruction ◽  
3D Face ◽  
3D Shape ◽  
Face Reconstruction ◽  
Lighting Conditions ◽  
3D Shape Reconstruction ◽  
3D Shapes ◽  
2D Images

3D face reconstruction is a popular area within the computer vision domain. 3D face reconstruction should ideally be achieved easily and cost-effectively, without requiring specialized equipment to estimate 3D shapes. As a result of this, many techniques for retrieving 3D shapes from 2D images have been proposed. In this paper, a novel method for 3D face reconstruction based on photometric stereo, which estimates the surface normal from shading information in multiple images, hence recovering the 3D shape of a face, is proposed. In order to overcome the problems of previous approaches related to prior-knowledge regarding lighting conditions and iterative algorithms, the exemplar is synthesized with known lighting conditions from at least three images, under arbitrary lighting conditions and using an illumination reference. Experiments in 3D face reconstruction were made by verifying the proposed approach using the illumination subset of the Max-Planck Institute face database and Yale face database B. Experimental results demonstrate that the proposed method is effective for 3D shape reconstruction of faces from 2D images.

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