Subspace distance analysis with application to adaptive Bayesian algorithm for face recognition
作者： Liwei Wang , Xiao Wang , Jufu Feng
DOI: 10.1016/J.PATCOG.2005.08.015
关键词:
摘要: We propose subspace distance measures to analyze the similarity between intrapersonal face subspaces, which characterize variations images of same individual. call conventional average (AIS) because image differences often come from a large number persons. An is referred as specific (SIS) if are just one person. demonstrate that SIS varies significantly person person, and most SISs not similar AIS. Based on these observations, we introduce maximum posteriori (MAP) adaptation problem estimation, apply it Bayesian recognition algorithm. Experimental results show adaptive algorithm outperforms non-adaptive well Eigenface Fisherface methods small available.
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sci-hub.se 参考文章(20)
PC Woodland, Speaker adaptation for continuous density HMMs: a review ISCA: International Speech Communication Association. ,(2001)
Richard Courant, David Hilbert, Methods of Mathematical Physics ,(1947)
Keinosuke Fukunaga, Introduction to statistical pattern recognition (2nd ed.) Academic Press Professional, Inc.. ,(1990)
O. Yamaguchi, E. Fukui, K. Maeda, Face recognition using temporal image sequence ieee international conference on automatic face and gesture recognition. pp. 318- 323 ,(1998) , 10.1109/AFGR.1998.670968
Harold Hotelling, Relations Between Two Sets of Variates Springer Series in Statistics. ,vol. 28, pp. 162- 190 ,(1992) , 10.1007/978-1-4612-4380-9_14
P.J. Phillips, Hyeonjoon Moon, S.A. Rizvi, P.J. Rauss, The FERET evaluation methodology for face-recognition algorithms IEEE Transactions on Pattern Analysis and Machine Intelligence. ,vol. 22, pp. 1090- 1104 ,(2000) , 10.1109/34.879790
Baback Moghaddam, Tony Jebara, Alex Pentland, Bayesian face recognition Pattern Recognition. ,vol. 33, pp. 1771- 1782 ,(2000) , 10.1016/S0031-3203(99)00179-X
M.A. Turk, A.P. Pentland, Face recognition using eigenfaces computer vision and pattern recognition. pp. 586- 591 ,(1991) , 10.1109/CVPR.1991.139758
C.-H. Lee, C.-H. Lin, B.-H. Juang, A study on speaker adaptation of the parameters of continuous density hidden Markov models IEEE Transactions on Signal Processing. ,vol. 39, pp. 806- 814 ,(1991) , 10.1109/78.80902
L. Wof, A. Shashua, Kernel principal angles for classification machines with applications to image sequence interpretation computer vision and pattern recognition. ,vol. 1, pp. 635- 640 ,(2003) , 10.1109/CVPR.2003.1211413