A Content-aware Filtering for RGBD Faces

Leandro Dihl, Leandro Cruz, Nuno Monteiro, Nuno Gonçalves

2019

Abstract

3D face models are widely used for several purposes, such as biometric systems, face verification, facial expression recognition, 3D visualization, and so on. They can be captured by using different kinds of devices, like plenoptic cameras, structured light cameras, time of flight, among others. Nevertheless, the models generated by all these consumer devices are quite noisy. In this work, we present a content-aware filtering for 2.5D meshes of faces that preserves their intrinsic features. This filter consists on an exemplar-based neighborhood matching where all models are in a frontal position avoiding rotation and perspective. We take advantage of prior knowledge of the models (faces) to improve the comparison. We first detect facial feature points, create the point correctors for regions of each feature, and only use the correspondent regions for correcting a point of the filtered mesh. The model is invariant to depth translation and scale. The proposed method is evaluated on a public 3D face dataset with different levels of noise. The results show that the method is able to remove noise without smoothing the sharp features of the face.

Paper Citation

in Harvard Style

Dihl L., Cruz L., Monteiro N. and Gonçalves N. (2019). A Content-aware Filtering for RGBD Faces. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 1: GRAPP; ISBN 978-989-758-354-4, SciTePress, pages 270-277. DOI: 10.5220/0007384702700277

in Bibtex Style

@conference{grapp19,
author={Leandro Dihl and Leandro Cruz and Nuno Monteiro and Nuno Gonçalves},
title={A Content-aware Filtering for RGBD Faces},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 1: GRAPP},
year={2019},
pages={270-277},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007384702700277},
isbn={978-989-758-354-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 1: GRAPP
TI - A Content-aware Filtering for RGBD Faces
SN - 978-989-758-354-4
AU - Dihl L.
AU - Cruz L.
AU - Monteiro N.
AU - Gonçalves N.
PY - 2019
SP - 270
EP - 277
DO - 10.5220/0007384702700277
PB - SciTePress