High-order local normal derivative pattern (LNDP) for 3D face recognition

Abstract

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.