TY - GEN
T1 - Kinect-based automatic 3D high-resolution face modeling
AU - Sun, Qi
AU - Tang, Yanlong
AU - Hu, Ping
AU - Peng, Jingliang
N1 - Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2012
Y1 - 2012
N2 - Microsoft Kinect can be used to capture both depth and color information and has been increasingly used for 3D modeling purposes. However, prior facial modeling methods either are computationally intensive or they generate rough results limited by the low resolution and instability of Kinect. In this paper, we propose a novel scheme for automatically and efficiently constructing a life-like textured 3D high-resolution model for the face of any user in front of a Kinect. Specifically, this scheme is composed of a sequence of steps including head region segmentation, depth and color image registration, resolution enhancement and 3D model fairing. Compared to prior methods, our scheme has a set of distinctive advantages. It can be robust even when the user is in a noisy environment; all the processes are automatic, which means that users need not interactively select feature points, and the energy optimization step is more efficient for fast processing of large-scale dynamic images.
AB - Microsoft Kinect can be used to capture both depth and color information and has been increasingly used for 3D modeling purposes. However, prior facial modeling methods either are computationally intensive or they generate rough results limited by the low resolution and instability of Kinect. In this paper, we propose a novel scheme for automatically and efficiently constructing a life-like textured 3D high-resolution model for the face of any user in front of a Kinect. Specifically, this scheme is composed of a sequence of steps including head region segmentation, depth and color image registration, resolution enhancement and 3D model fairing. Compared to prior methods, our scheme has a set of distinctive advantages. It can be robust even when the user is in a noisy environment; all the processes are automatic, which means that users need not interactively select feature points, and the energy optimization step is more efficient for fast processing of large-scale dynamic images.
KW - Kinect
KW - face modeling
KW - super-resolution
UR - http://www.scopus.com/inward/record.url?scp=84874567455&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84874567455&partnerID=8YFLogxK
U2 - 10.1109/IASP.2012.6425065
DO - 10.1109/IASP.2012.6425065
M3 - Conference contribution
AN - SCOPUS:84874567455
SN - 9781467325455
T3 - Proceedings of 2012 International Conference on Image Analysis and Signal Processing, IASP 2012
SP - 382
EP - 385
BT - Proceedings of 2012 International Conference on Image Analysis and Signal Processing, IASP 2012
T2 - 2012 4th International Conference on Image Analysis and Signal Processing, IASP 2012
Y2 - 9 November 2012 through 11 November 2012
ER -