TY - GEN
T1 - Fast high dimensional vector multiplication face recognition
AU - Barkan, Oren
AU - Weill, Jonathan
AU - Wolf, Lior
AU - Aronowitz, Hagai
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - diffusion maps
KW - dimensionality reduction
KW - face recognition
KW - high dimensional representation
KW - overcomplete representation
KW - pattern recognition
KW - unsupervised labeling
UR - http://www.scopus.com/inward/record.url?scp=84898819467&partnerID=8YFLogxK
U2 - 10.1109/ICCV.2013.246
DO - 10.1109/ICCV.2013.246
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AN - SCOPUS:84898819467
SN - 9781479928392
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 1960
EP - 1967
BT - Proceedings - 2013 IEEE International Conference on Computer Vision, ICCV 2013
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2013 14th IEEE International Conference on Computer Vision, ICCV 2013
Y2 - 1 December 2013 through 8 December 2013
ER -