Local feature tensor based deep learning for 3D face recognition

Shisong Lin, Feng Liu, Yahui Liu, Linlin Shen

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

A local feature tensor similarity based deep learning approach is proposed in this paper for 3D face recognition. Once a set of salient points on the 3D mesh are detected, three scale and rotation invariant features are extracted to represent local surface around each salient point. The local features of all the salient points are concatenated to produce a 3rd order feature tensor to represent a 3D face. Similarity of two 3D faces can thus be measured by a similarity tensor calculated using the two feature tensors. To address the unavailability of large 3D face samples, a feature tensor based data augmentation approach is proposed to augment the number of feature tensors. Experimental results show that the ResNet model trained using the augmented feature tensors achieves the best performance among state of the art competitors, i.e. 99.71% and 96.2% accuracy are achieved for Bosphorus and BU3DFE database, respectively.

Original languageEnglish
Title of host publicationProceedings - 14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728100890
DOIs
Publication statusPublished - May 2019
Externally publishedYes
Event14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019 - Lille, France
Duration: 14 May 201918 May 2019

Publication series

NameProceedings - 14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019

Conference

Conference14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019
Country/TerritoryFrance
CityLille
Period14/05/1918/05/19

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Media Technology

Fingerprint

Dive into the research topics of 'Local feature tensor based deep learning for 3D face recognition'. Together they form a unique fingerprint.

Cite this