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Konrad, and P. Ishwar, \u201cDynamic time warping for gesture-based user identification and authentication with kinect,\u201d IEEE International Conference on Acoustics, Speech and Signal Processing, pp.2371-2375, 2013. 10.1109\/icassp.2013.6638079","DOI":"10.1109\/ICASSP.2013.6638079"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] E. Hayashi, M. Maas, and J.I. Hong, \u201cWave to me: User identification using body lengths and natural gestures,\u201d Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp.3453-3462, 2014. 10.1145\/2556288.2557043","DOI":"10.1145\/2556288.2557043"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] J. Wu, J. Konrad, and P. Ishwar, \u201cThe value of multiple viewpoints in gesture-based user authentication,\u201d Proc. IEEE Conf. 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Blake, \u201cEfficient human pose estimation from single depth images,\u201d IEEE Trans. Pattern Analysis and Machine Intelligence, vol.35, no.12, pp.2821-2840, 2013. 10.1109\/tpami.2012.241","DOI":"10.1109\/TPAMI.2012.241"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] O. Boiman, E. Shechtman, and M. Irani, \u201cIn defense of nearest-neighbor based image classification,\u201d Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), pp.1-8, 2008. 10.1109\/cvpr.2008.4587598","DOI":"10.1109\/CVPR.2008.4587598"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] J. Gall, A. Yao, N. Razavi, L. Van Gool, and V. Lempitsky, \u201cHough forests for object detection, tracking, and action recognition,\u201d IEEE Trans. Pattern Analysis and Machine Intelligence, vol.33, no.11, pp.2188-2202, 2011. 10.1109\/tpami.2011.70","DOI":"10.1109\/TPAMI.2011.70"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] L. Wang, T. Tan, H. Ning, and W. Hu, \u201cSilhouette analysis-based gait recognition for human identification,\u201d IEEE Trans. Pattern Analysis and Machine Intelligence, vol.25, no.12, pp.1505-1518, 2003. 10.1109\/tpami.2003.1251144","DOI":"10.1109\/TPAMI.2003.1251144"},{"key":"13","unstructured":"[13] P.V.C. Hough, \u201cMachine analysis of bubble chamber pictures,\u201d International Conference on High-Energy Accelerators and Instrumentation, pp.554-558, 1959."},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] R.O. Duda and P.E. Hart, \u201cUse of the hough transformation to detect lines and curves in pictures,\u201d Communications of the ACM, vol.15, no.1, pp.11-15, 1972. 10.1145\/361237.361242","DOI":"10.1145\/361237.361242"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] D.H. Ballard, \u201cGeneralizing the hough transform to detect arbitrary shapes,\u201d Pattern Recognition, vol.13, no.2, pp.111-122, 1981. 10.1016\/0031-3203(81)90009-1","DOI":"10.1016\/0031-3203(81)90009-1"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] B. Leibe and B. Schiele, \u201cInterleaved object categorization and segmentation,\u201d British Machine Vision Conf., pp.759-768, 2003. 10.5244\/c.17.78","DOI":"10.5244\/C.17.78"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] S. Maji and J. Malik, \u201cObject detection using a max-margin hough transform,\u201d Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), pp.1038-1045, 2009. 10.1109\/cvpr.2009.5206693","DOI":"10.1109\/CVPR.2009.5206693"},{"key":"18","doi-asserted-by":"publisher","unstructured":"[18] L. Breiman, \u201cRandom forests,\u201d Machine Learning, vol.45, no.1, pp.5-32, 2001. 10.1023\/a:1010933404324","DOI":"10.1023\/A:1010933404324"},{"key":"19","unstructured":"[19] T. Minka, \u201cThe \u2018summation hack\u2019 as an outlier model,\u201d Microsoft Research Technical Report, 2003."},{"key":"20","doi-asserted-by":"publisher","unstructured":"[20] M. Muja and D.G. Lowe, \u201cScalable nearest neighbor algorithms for high dimensional data,\u201d IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.36, no.11, pp.2227-2240, 2014. 10.1109\/tpami.2014.2321376","DOI":"10.1109\/TPAMI.2014.2321376"},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] L. Xia, C.-C. Chen, and J. Aggarwal, \u201cView invariant human action recognition using histograms of 3d joints,\u201d Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), pp.20-27, 2012. 10.1109\/cvprw.2012.6239233","DOI":"10.1109\/CVPRW.2012.6239233"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] W. Li, Z. Zhang, and Z. 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