iccv iccv2013 iccv2013-158 iccv2013-158-reference knowledge-graph by maker-knowledge-mining
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Author: Oren Barkan, Jonathan Weill, Lior Wolf, Hagai Aronowitz
Abstract: This paper advances descriptor-based face recognition by suggesting a novel usage of descriptors to form an over-complete representation, and by proposing a new metric learning pipeline within the same/not-same framework. First, the Over-Complete Local Binary Patterns (OCLBP) face representation scheme is introduced as a multi-scale modified version of the Local Binary Patterns (LBP) scheme. Second, we propose an efficient matrix-vector multiplication-based recognition system. The system is based on Linear Discriminant Analysis (LDA) coupled with Within Class Covariance Normalization (WCCN). This is further extended to the unsupervised case by proposing an unsupervised variant of WCCN. Lastly, we introduce Diffusion Maps (DM) for non-linear dimensionality reduction as an alternative to the Whitened Principal Component Analysis (WPCA) method which is often used in face recognition. We evaluate the proposed framework on the LFW face recognition dataset under the restricted, unrestricted and unsupervised protocols. In all three cases we achieve very competitive results.
[1] G. B. Huang, M. Ramesh, T. Berg and E. Learned-Miller,
[2] Z. Cao, Q. Yin, X. Tang and J. Sun,
[3] Q. Yin, X. Tang and J. Sun,
[4] E. Nowak and F. Jurie,
[5] T. Berg and P. N. Belhumeur,
[6] H. V. Nguyen and L. Bai,
[7] M. Guillaumin, J. Verbeek and C. Schmid,
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
[18]
[19] Conference on Computer Vision (ICCV), 2009. N. Kumar, A. C. Berg, P. N. Belhumeur and S. K. Nayar,
[20]
[21]
[22]
[23]
[24] Advances in Biometrics, 2007. J. Bruna and S. Mallat,
[25] Y. Taigman and L. Wolf,
[26] P. Li, Y. Fu, U. Mohammed, J. H. Elder and S. J.D.Prince,
[27]
[28] S. u. Hussain, T. Napoléon and F. Jurie,
[29] C. Huang, S. Zhu and K. Yu,
[30] D. Chen, X. Cao, F. Wen and J. Sun,
[31] D. Chen, X. Cao, L. Wang, F. Wen and J. Sun,