Paper Info
Title
Context constrained facial landmark localization based on discontinuous Haar-like feature
Abstract
Facial landmark localization is well known as one of the bottlenecks in face recognition. This paper proposes a novel facial landmark localization method, which introduces facial context constrains into cascaded AdaBoost framework. The motivation of our method lies in the basic human physiology observation that not only the local texture information but also the global context information is used together for human to realize the landmark location task. Therefore, in our solution, a novel type of Haar-like feature, called discontinuous Haar-like feature, is proposed to characterize the facial context, i.e. the cooccurrence relationship between target facial landmark and other local texture patterns within face region (including other landmarks, facial organs and also smoothing regions). For the locating task, traditional Haar-like features (characterizing local texture information) and discontinuous Haar-like features (characterizing context constrains in global sense) are combined together to form more powerful representations. Through Real AdaBoost learning, distinctive features are selected automatically and used for facial landmark detection. Our experiments on BioID and Cohn-Kanade databases have validated the proposed method by comparing with other state-of-the-art results.
Year
DOI
Venue
2011
10.1109/FG.2011.5771329
FG
Keywords
Field
DocType
context constraints,discontinuous haar-like feature,face recognition,facial landmark localization,databases,learning artificial intelligence,pixel,face,feature extraction,accuracy
Computer vision,Facial recognition system,AdaBoost,Pattern recognition,Haar,Human physiology,Feature extraction,Smoothing,Pixel,Artificial intelligence,Landmark,Geography
Conference
Citations 
PageRank 
References 
10
0.58
13
Authors
5
Name
Order
Citations
PageRank
Xiaowei Zhao1353.06
Xiujuan Chai241828.41
Zhiheng Niu3824.46
Cher-Keng Heng4282.22
Shiguang Shan56322283.75