In this paper, we present a novel approach for fusing shape and texture local binary patterns (LBP) for 3D face recognition. Using a recently proposed framework [1], we compute LBP directly on the face mesh surface, then we construct a grid of the regions on the facial surface that can accommodate global and partial descriptions. Compared to its depthimage counterpart, our approach is distinguished by the following features: a) inherits the intrinsic advantages of mesh surface (e.g., preservation of the full geometry); b) does not require normalization; c) can accommodate partial matching. In addition, it allows early-level fusion of texture and shape modalities. Through experiments conducted on the BU-3DFE and Bosphorus databases, we assess different variants of our approach with regard to facial expressions and missing data. Index Terms-mesh-LBP, fusion, 3D face recognition 1. INTRODUCTION 3D face image has emerged as a promising modality for face recognition addressing the limitations of its 2D counterpart, such as pose and luminance variation, while opening-up new horizons for enhancing the reliability of face-based identification. This trend has been further fueled by the advances in 3D scanning technology, which provides now 3D textured scans encompassing aligned shape and photometric data. Recently, Local Binary Patterns (LBP) [2] descriptors have been extensively used in 2D face description and representation and rapidly have been extended to the 3D modality. 3D-LBP approaches advanced the state of the art and proved to be competitive with other classes of methods. However, their applications is hindered by the intrinsic limitations of the 2D image support. Indeed, most if not all 3D-LBP approaches operate on depth images, in which depth is mapped to a gray level via 2D projection. As such, depth images require normalization to accommodate with pose variation. Yet, they still remain vulnerable to self-occlusion (caused for instance by lateral areas of the nose). To address these problems, we propose a novel LBPbased face representation that can be constructed over triangular mesh manifolds. This representation, which is based on the recently proposed mesh-LBP concept [1] , relieves the recognition process from the need for normalization, while
Werghi et al. REF presented a novel approach to fuse shape and texture local binary patterns (LBPs) on a mesh for 3D face recognition.
Boosting 3D LBP-Based Face Recognition by Fusing Shape and Texture Descriptors on the Mesh
IEEE Trans. Information Forensics and Security
IEEE Trans. Information Forensics and Security
Computer Science