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

0Citations
Citations of this article
9Readers
Mendeley users who have this article in their library.
Get full text

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.

Cite

CITATION STYLE

APA

Freitas, T., Alves, P. G., Carpinteiro, C., Rodrigues, J., Fernandes, M., Castro, M., … Cardoso, J. S. (2016). A comparative analysis of deep and shallow features for multimodal face recognition in a novel RGB-D-IR dataset. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10072 LNCS, pp. 800–811). Springer Verlag. https://doi.org/10.1007/978-3-319-50835-1_72

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free