Browse > Article
http://dx.doi.org/10.6109/jkiice.2013.17.5.1119

A Study on High Speed Face Tracking using the GPGPU-based Depth Information  

Kim, Woo-Youl (광운대학교 전자재료공학과)
Seo, Young-Ho (광운대학교 교양학부)
Kim, Dong-Wook (광운대학교 전자재료공학과)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.17, no.5, 2013 , pp. 1119-1128 More about this Journal
Abstract
In this paper, we propose an algorithm to detect and track the human face with a GPU-based high speed. Basically the detection algorithm uses the existing Adaboost algorithm but the search area is dramatically reduced by detecting movement and skin color region. Differently from detection process, tracking algorithm uses only depth information. Basically it uses a template matching method such that it searches a matched block to the template. Also, In order to fast track the face, it was computed in parallel using GPU about the template matching. Experimental results show that the GPU speed when compared with the CPU has been increased to up to 49 times.
Keywords
face detection; face tracking; depth information; GPGPU; template matching;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 M. H. Yang, et al., "Detecting Faces in Images; A Survey," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 24, No. 1, pp. 34-58, Jan. 2002.   DOI   ScienceOn
2 G. Z. Yang and T. S. Huang, "Human Face Detection in Complex Background," Pattern Recognition, Vol. 27, No. 1, pp. 53-63, Jan. 1994.   DOI   ScienceOn
3 L. Craw, D. Tock, and A. Bennett, "Finding Face Features," Proc. Second European Conf. Computer Vision, pp. 92-96, 1992.
4 MESA SR4000 MESA Imaging, 2011 [Online]. Available:http://www.mesa-imaging.ch/prodview4k.php
5 Kinect Website, http://www.microsoft.com/en-us/kinectforwindows/
6 R. Cabido, "High-performance template tracking", J. Vis. Commun. Image, pp 271-286, 2012.
7 Oscar Mateo Lozano, Kazuhiro Otsuka, "SIMULTANEOUS AND FAST 3D TRACKING OF MULTIPLE FACES IN VIDEO BY GPU-BASED STREAM PROCESSING", ICASSP, 2008.
8 Claus Lenz, Giorgio Panin, Alois Knoll, "A GPU-accelerated particle filter with pixel-level likelihood", 2008.
9 GPGPU Website, http://gpgpu.org
10 Douglas Chai, et al., "Locating Facial Region of a Head-and -Shoulders Color Image," Int'l Conf. Automatic Face and Gesture Recognition, pp. 124-129, April 1998.
11 David B. Kirk and Wen-mei W. Hwu, "Programming Massively Parallel Processors: A Hands-on Approach 1st Ed", ELSEVIER
12 NVidia Website, http://www.nvidia.co.kr/
13 Mark Harris, "Optimizing Parallel Reduction in CUDA," NVIDIA Developer Technology, 2007.
14 X. Suau, J. Ruiz-Hidalgo and J. Casas, "Real-Time Head and Hand Tracking Based on 2.5D Data", IEEE Trans. Multimedia, Vol. 14, No. 3, pp. 575-585, June 2012.   DOI   ScienceOn
15 S.-K. Kwon and S.-W. Kim, "Motion Estimation Method by Using Depth Camera," J. Broadcast Engineering, Vol. 17, No. 4, pp. 676-683, Jul. 2012.   과학기술학회마을   DOI   ScienceOn
16 S.-K. Kwon, Y.-H. Park and K.-R. Kwon, "Zoom Motion Estimation Method by Using Depth Information," J. Korea Multimedia Society, Vol. 16, No. 2, pp. 131-137, Feb. 2013.   과학기술학회마을   DOI   ScienceOn
17 P. Viola and M. J. Jones, "Robust Real-Time Face Detection," Computer Vision, Vol. 52, No. 2, pp. 137-154, 2004.
18 G, Q, Zhao, et al., "A Simple 3D face Tracking Method based on Depth Information," Int'l Conf. on Machine Learning and Cybernetics, pp. 5022-5027, Aug. 2005.
19 C. X. Wang and Z. Y. Li, "A New Face Tracking Algorithm Based on Local Binary Pattern and Skin Color Information," ISCSCT, Vol. 2, pp. 20-22, Dec. 2008.