  Derived from a general definition of texture in a local neighborhood, local
directional pattern (LDP) encodes the directional information in the small
local 3x3 neighborhood of a pixel, which may fail to extract detailed
information especially during changes in the input image due to illumination
variations. Therefore, in this paper we introduce a novel feature extraction
technique that calculates the nth order direction variation patterns, named
high order local directional pattern (HOLDP). The proposed HOLDP can capture
more detailed discriminative information than the conventional LDP. Unlike the
LDP operator, our proposed technique extracts nth order local information by
encoding various distinctive spatial relationships from each neighborhood layer
of a pixel in the pyramidal multi-structure way. Then we concatenate the
feature vector of each neighborhood layer to form the final HOLDP feature
vector. The performance evaluation of the proposed HOLDP algorithm is conducted
on several publicly available face databases and observed the superiority of
HOLDP under extreme illumination conditions.
