Performing facial recognition between Near Infrared (NIR) and visible-light (VIS) images has been established as a common method of countering illumination variation problems in face recognition. In this paper we present a new database to enable the evaluation of cross-spectral face recognition. A series of preprocessing algorithms, followed by Local Binary Pattern Histogram (LBPH) representation and combinations with Linear Discriminant Analysis (LDA) are used for recognition. These experiments are conducted on both NIR→VIS and the less common VIS→NIR protocols, with permutations of uni-modal training sets. 12 individual baseline algorithms are presented. In addition, the best performing fusion approaches involving a subset of 12 algorithms are also described.
REF incorporate a series of preprocessing methods to do normalization, then combine Local Binary Pattern Histogram (LBPH) representation with LDA to extract robust features.
Evaluation of face recognition system in heterogeneous environments (visible vs NIR)
2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)
2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)
Computer Science