3D face verification across pose based on euler rotation and tensors

Abstract

In this paper, we propose a new approach for 3D face verification based on tensor representation. Face challenges, such as illumination, expression and pose, are modeled as a multilinear algebra problem where facial images are represented as high order tensors. Particularly, to account for head pose variations, several pose scans are generated from a single depth image using the Euler transformation. Multi-bloc local phase quantization (MB-LPQ) histogram features are extracted from depth face images and arranged as a third order tensor. The dimensionality of the tensor is reduced based on the higher-order singular value decomposition (HOSVD). HOSVD projects the input tensor in a new subspace in which the dimension of each tensor mode is reduced. To discriminate faces from different persons, we utilize the Enhanced Fisher Model (EFM). Experimental evaluations on CASIA-3D database, which contains large head pose variations, demonstrate the effectiveness of the proposed approach. A verification rate of 98.60% is obtained. Index Terms 3D face verification, tensor analysis, multilinear dimensionality reduction, Euler angles. I. INTRODUCTION In real world applications, face recognition is confronted to several challenges, in terms of, for e.g., illuminations, expressions, occlusions and poses variations. This situation has highly motivated researchers from the fields of computer vision and pattern recognition to consider face recognition challenge under so called 'in the wild' conditions, referring to face images acquired A. Chouchane, A.