۱- Introduction

۲- Related work

۳- Proposed RGB-D face recognition approach

۴- Experimental results

۵- Conclusion

References

Abstract

In this article we present a novel RGB-D learned local representations for face recognition based on facial patch description and matching. The major contribution of the proposed approach is an efficient learning and combination of data-driven descriptors to characterize local patches extracted around image reference points. We explored the complementarity between both of deep learning and statistical image features as data-driven descriptors. In addition, we proposed an efficient high-level fusion scheme based on a sparse representation algorithm to leverage the complementarity between image and depth modalities and also the used data-driven features. Our approach was extensively evaluated on four well-known benchmarks to prove its robustness against known challenges in the case of face recognition. The obtained experimental results are competitive with the state-of-the-art methods while providing a scalable and adaptive RGB-D face recognition method.

Introduction

Face recognition for an automated person identification has received great attention over the years as it offers the most user-friendly and non-invasive modality. Face recognition based on standard two dimensional (2-D) images was extensively studied but it still suffers from problems related to imaging conditions and face pose variations. Thanks to the progress in three-dimensional (3-D) technology, recent research has shifted from 2-D to 3-D (Abbad et al., 2018). Indeed, 3-D face representation ensures a reliable surface shape description and adds geometric shape information to the face characterization. Most recently, some researchers proposed to use image and depth data captured from cost-effective RGB-D sensors like MS Kinect or Intel RealSense instead of bulky and expensive 3-D scanners. In addition to color images, RGB-D sensors provide depth maps describing the scene 3-D shape by active vision or an alternative technology. Driven by the emergence of this type of sensors and the latest advances in deep learning techniques, RGB-D face recognition is now becoming at the heart of several recent research studies. Indeed, it is nowadays crystal clear that data-driven feature extraction, using Convolutional Neural Networks (CNNs) for example, outperforms traditional hand-crafted features for many computer vision tasks like object detection (Szegedy et al., 2013), image clas sification (Krizhevsky et al., 2012), etc. When it comes to the RGB-D face recognition, the observed challenges basically deal with face pose variations, partial occlusions, imaging conditions, and discriminant feature extraction.