Robust Facial Landmark Localization Using LBP Histogram Correlation Based Initialization

No Thumbnail Available
File version
Author(s)
Pan, Yiyun
Zhou, Junwei
Gao, Yongsheng
Xiang, Jianwen
Xiong, Shengwu
Yang, Yanchao
Griffith University Author(s)
Primary Supervisor
Other Supervisors
Editor(s)
Randall Bilof
Date
2017
Size
File type(s)
Location
Washington, DC
License
Abstract

Facial landmark localization on images with occlusions is an important and challenging task in many visual applications. Recently, the cascaded pose regression has attracted increasing attention, since it achieved superior performance in terms of facial landmark localization under occlusions. However, such approach is sensitive to initialization, where an improper initialization will decrease the performance sharply. In this paper, we propose a novel initialization method to get a robust initial shape by analysing correlation of Local Binary Patterns (LBP) histograms between the estimated face and training faces. The shape of the training face that is most correlated with the estimated face, will be selected as the initialization for the regression. The selected shape is closer to the real shape of the estimated face, which makes the landmark localization more accurate. Besides, in order to make the initial shape more robust to occlusions, we propose a boosted smart restarts technique by checking location and occlusion jointly instead of checking location only. We show that the proposed method significantly improves performance over existing landmark localization methods on the challenging dataset of COFW. The experimental results demonstrate that the proposed method reduces error by 11.9% and failure cases by 20.8% on COFW dataset. Moreover, it detects face occlusions with 85/40% precision/recall.

Journal Title
Conference Title
2017 12TH IEEE INTERNATIONAL CONFERENCE ON AUTOMATIC FACE AND GESTURE RECOGNITION (FG 2017)
Book Title
Edition
Volume
Issue
Thesis Type
Degree Program
School
Publisher link
Patent number
Funder(s)
Grant identifier(s)
Rights Statement
Rights Statement
Item Access Status
Note
Access the data
Related item(s)
Subject
Artificial intelligence not elsewhere classified
Persistent link to this record
Citation