Fast High Dimensional Vector Multiplication Face Recognition

• Published in: 2013 IEEE International Conference on Computer Vision pp. 1960 - 1967
• Main Authors: Barkan, Oren, Weill, Jonathan, Wolf, Lior, Aronowitz, Hagai
• Format: Conference Proceeding Journal Article
• Language: English
• Published: IEEE 01.12.2013
• Subjects: Accuracy; Classification algorithms; Computer vision; Diffusion; diffusion maps; dimensionality reduction; Discriminant analysis; Face; Face recognition; high dimensional representation; Nonlinearity; overcomplete representation; pattern recognition; Principal component analysis; Recognition; Reduction; Representations; Scattering; Training; unsupervised labeling; Vectors
• ISSN: 1550-5499; 2380-7504
• DOI: 10.1109/ICCV.2013.246

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.