A Discriminative Multi-Channel Facial Shape (MCFS) Representation and Feature Extraction for 3D Human Faces Academic Article uri icon

abstract

  • AbstractBuilding an effective representation for 3D face geometry is essential for face analysis tasks, that is, landmark detection, face recognition and reconstruction. This paper proposes to use a MultiChannel Facial Shape (MCFS) representation that consists of depth, handengineered feature and attention maps to construct a 3D facial descriptor. And, a multichannel adjustment mechanism, named filtered squeeze and reversed excitation (FSRE), is proposed to reorganize MCFS data. To assign a suitable weight for each channel, FSRE is able to learn the importance of each layer automatically in the training phase. MCFS and FSRE blocks collaborate together effectively to build a robust 3D facial shape representation, which has an excellent discriminative ability. Extensive experimental results, testing on both highresolution and lowresolution face datasets, show that facial features extracted by our framework outperform existing methods. This representation is stable against occlusions, data corruptions, expressions and pose variations. Also, unlike traditional methods of 3D face feature extraction, which always take minutes to create 3D features, our system can run in real time.

published proceedings

  • COMPUTER GRAPHICS FORUM

altmetric score

  • 1

author list (cited authors)

  • Gong, X., Li, X., Li, T., & Liang, Y.

citation count

  • 0

complete list of authors

  • Gong, Xun||Li, Xin||Li, Tianrui||Liang, Yongqing

publication date

  • September 2020

publisher