@inproceedings{54985d148e444a299b449026f7ee1226,
title = "Fast high dimensional vector multiplication face recognition",
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.",
keywords = "diffusion maps, dimensionality reduction, face recognition, high dimensional representation, overcomplete representation, pattern recognition, unsupervised labeling",
author = "Oren Barkan and Jonathan Weill and Lior Wolf and Hagai Aronowitz",
year = "2013",
doi = "10.1109/ICCV.2013.246",
language = "الإنجليزيّة",
isbn = "9781479928392",
series = "Proceedings of the IEEE International Conference on Computer Vision",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1960--1967",
booktitle = "Proceedings - 2013 IEEE International Conference on Computer Vision, ICCV 2013",
address = "الولايات المتّحدة",
note = "2013 14th IEEE International Conference on Computer Vision, ICCV 2013 ; Conference date: 01-12-2013 Through 08-12-2013",
}