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http://dx.doi.org/10.5762/KAIS.2012.13.8.3666

Evaluation of Depth Image of IR Range Sensor with Face Recognition Algorithms  

Kwon, Ki-Hyeon (Dept. of Information & Communication Engineering, Kangwon National University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.13, no.8, 2012 , pp. 3666-3671 More about this Journal
Abstract
We evaluate the face detection and recognition of depth image that is obtained by infrared range sensor. and Face recognition was usually focused on accuracy aspect but it is not enough to evaluate the performance in testing for real world application. In this paper, we evaluate the overall performance like accuracy, training, test speed and memory use for the well known face recognition algorithm like PCA, LDA, ICA and SVM. This experiment evaluate the good results of depth and colored depth image compatible with the colored image although the file size of depth and colored depth image is 30%~40% less than the colored image. Whereas, LDA got the good accuracy performance next to the SVM and also shows the good performance in speed and the amount of memory.
Keywords
Range Sensor; Depth Image; ICA; LDA; ICA; SVM;
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  • Reference
1 Kinect. http://www.xbox.com/en-us/kinect/ March 2011.
2 W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, "Face Recognition: A Literature Survey", ACM Computing Surveys, vol. 35, pp. 399-458, 2003.   DOI   ScienceOn
3 M. A. Turk and A. P. Pentland, "Face Recognition Using Eigenfaces", in IEEE CVPR, pp. 586-591, 1991.
4 P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, "Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection", in IEEE TPAMI. vol. 19, pp. 711-720, 1997.   DOI   ScienceOn
5 M. S. Bartlett, J. R. Movellan, and T. J. Sejnowski, "Face Recognition by Independent Component Analysis", IEEE Transactions on Neural Networks, vol. 13, pp. 1450-1464, 2002.   DOI   ScienceOn
6 G. Guo, S. Z. Li, and K. Chan, "Face Recognition By Support Vector Machines", Image and Vision Computing, vol. 19, pp. 631-638, 2001.   DOI
7 P. J. Phillips, H. Moon, S. A. Rizvi, and P. J. Rauss, "The FERET Evaluation Methodology for Face-Recognition Algorithms", IEEE TPAMI, vol. 22, pp. 1090-1104, 2000.   DOI   ScienceOn
8 Y. Hu, D. Jiang, S. Yan, and L. Zhang, "Automatic 3D Reconstruction for Face Recognition", in IEEE FG, pp. 843-848, 2004.
9 Freedman, B., Shpunt, A., Machline, M., Arieli, Y., "Depth mapping using projected patterns", Prime Sense Ltd, United States 2010.
10 P. Viola and M. Jones, "Rapid Object Detection Using a Boosted Cascade of Simple Features", in IEEE CVPR, pp. 511.518, 2001.
11 D. S. Bolme, J. R. Beveridge, M. Teixeira, and B. A. Draper, "The CSU Face Identification Evaluation System: Its Purpose, Features, and Structure", in ICVS Graz, Austria, 2003.
12 J. Weng, Y. Zhang, and W. S. Hwang, "Candid Covariance-Free Incremental Principal Component Analysis", IEEE TPAMI, vol. 25, pp. 1034-1040, 2003.   DOI   ScienceOn
13 X. Liu, T. Chen, and S. M. Thornton, "Eigenspace Updating for Non-Stationary Process and Its Application to Face Recognition", Pattern Recognition, vol. 36, pp. 1945-1959, 2003.   DOI   ScienceOn
14 B. Heisele, P. Ho, and T. Poggio, "Face Recognition with Support Vector Machines: Global versus Component-Based Approach", in ICCV. vol. 2 Vancouver, Canada, pp. 688.694, 2001.