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

Pattern recognition (psychology)Facial recognition systemNormalization (sociology)Artificial intelligenceLinear discriminant analysisComputer scienceDimensionality reductionPrincipal component analysisDiffusion mapFace (sociological concept)Nonlinear dimensionality reduction

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Publication Info

Year
2013
Type
article
Pages
1960-1967
Citations
167
Access
Closed

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Oren Barkan, Jonathan Weill, Lior Wolf et al. (2013). Fast High Dimensional Vector Multiplication Face Recognition. , 1960-1967. https://doi.org/10.1109/iccv.2013.246

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DOI
10.1109/iccv.2013.246