A comparative analysis of deep and shallow features for multimodal face recognition in a novel RGB-D-IR dataset

Abstract

With new trends like 3D and deep learning alternatives for face recognition becoming more popular, it becomes essential to establish a complete benchmark for the evaluation of such algorithms, in a wide variety of data sources and non-ideal scenarios. We propose a new RGB-depth-infrared (RGB-D-IR) dataset, RealFace, acquired with the novel Intel® RealSense TM collection of sensors, and characterized by multiple variations in pose, lighting and disguise. As baseline for future works, we assess the performance of multiple deep and “shallow” feature descriptors. We conclude that our dataset presents some relevant challenges and that deep feature descriptors present both higher robustness in RGB images, as well as an interesting margin for improvement in alternative sources, such as depth and IR.