This paper proposes a novel descriptor based on the local derivative
pattern (LDP) for 3D face recognition. Compared to the local binary
pattern (LBP), LDP can capture more detailed information by encoding
directional pattern features. It is based on the local derivative
variations that extract high-order local information. We propose a
novel discriminative facial shape descriptor, local normal derivative
pattern (LNDP) that extracts LDP from the surface normal. Using
surface normal, the orientation of a surface at each point is determined
as a first-order surface differential. Three normal component
images are extracted by estimating three components of normal vectors
in x, y, and z channels. Each normal component is divided into
several patches and encoded using LDP. The final descriptor is created
by concatenating histograms of the LNDP on each patch. Experimental
results on two famous 3D face databases, FRGC v2.0 and
Bosphorus illustrate the effectiveness of the proposed descriptor.