Multiple patterns of gradient magnitudes for face recognition

Describing efficiently faces is a task of mounting importance. Most of existing algorithms do not address all the three criteria, i.e., robustness, distinctiveness and low computational cost. Inspired by recent features so-called POEM (Patterns of Oriented Edge Magnitudes) which is argued balancing well the three concerns, we first provide an improvement to it and then propose novel features so-called self-POEM considering the relations between edge distributions along different directions of one region itself. Self-POEM provides the complementary strength which is not present in POEM. Combining them together, a more robust algorithm is obtained and by experiments we prove that our approach is more efficient than contemporary ones.