By Topic

Subface hidden Markov models coupled with a universal occlusion model for partially occluded face recognition

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $33
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
S. -m. Huang ; Advanced Optoelectronic Technology Center, Institute of Computer and Communication Engineering, Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan ; J. -f. Yang

In this study, a novel face recognition framework based on the grammatical face models has been proposed to tackle partial occlusion problem. The grammatical face model represents a face by five isolated `subface`, forehead, eyes, nose, mouth and chin models in cooperation with `occlusion` models. With the creations of `subface` and `occlusion` models, the authors then define a facial grammar to manipulate `subface` and `occlusion` models for constructing various composite face models structurally. Furthermore, the authors also introduce a universal `occlusion` model, which could handle general occlusions to improve the robustness and flexibility of grammatical face models. The proposed face recognition system could overcome two problems. One is to resolve the problem of face recognition with partial occlusions; the other is to overcome a challenge of training face models from occluded face images only. Experimental results carried out on AR facial database reveal that the proposed approach outperforms the state-of-the-art methods.

Published in:

IET Biometrics  (Volume:1 ,  Issue: 3 )