Weight-Optimal Local Binary Patterns

In this work, we have proposed a learning paradigm for obtaining weight-optimal local binary patterns (WoLBP). We first reformulate the LBP problem into matrix multiplication with all the bitmaps flattened and then resort to the Fisher ratio criterion for obtaining the optimal weight matrix for LBP encoding. The solution is closed form and can be easily solved using one eigen-decomposition. The experimental results on the FRGC ver2.0 database have shown that the WoLBP gains significant performance improvement over traditional LBP, and such WoLBP learning procedure can be directly ported to many other LBP variants to further improve their performances.