Curtin University Homepage
  • Library
  • Help
    • Admin

    espace - Curtin’s institutional repository

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item

    Accurate Facial Landmarks Detection for Frontal Faces with Extended Tree-Structured Models

    Access Status
    Fulltext not available
    Authors
    Liang, Antoni
    Liu, Wan-Quan
    Li, Ling
    Farid, M.
    Le, V.
    Date
    2014
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Liang, A. and Liu, W. and Li, L. and Farid, M. and Le, V. 2014. Accurate Facial Landmarks Detection for Frontal Faces with Extended Tree-Structured Models, in M. Felsberg (ed), ICPR 22nd International Conference on Pattern Recognition, Aug 24-28 2014, pp. 538-543. Stockholm, Sweden: IEEE Computer Society.
    Source Title
    2014 22nd International Conference on Pattern Recognition (ICPR)
    Source Conference
    ICPR 22nd International Conference on Pattern Recognition
    DOI
    10.1109/ICPR.2014.103
    ISSN
    1051-4651
    School
    Department of Computing
    URI
    http://hdl.handle.net/20.500.11937/19239
    Collection
    • Curtin Research Publications
    Abstract

    In this paper, we aim to improve one of the current state-of-the-art models for facial components detection/localization. The objectives are to increase the amount of landmark points detected and improve the landmark extraction accuracy for frontal faces. The model is following Zhu and Ramanan's approach with a tree-structure. The popular AR dataset is chosen as an alternative training dataset as it provides more landmark points requested. Our extension models are compared with Zhu and Ramanan's frontal face models in terms of detection accuracy. We also compare our models with another robust facial components detector called CompASM. Our experiments show that our models can achieve lower error rate on some fiducial points by providing more landmarks, and these accurate fiducial points will provide more accurate features for some applications related to facial shapes. The impact of image colour spaces other than RGB on the proposed detector is also investigated.

    Related items

    Showing items related by title, author, creator and subject.

    • Robust and flexible landmarks detection for uncontrolled frontal faces in the wild
      Liang, A.; Wang, C.; Liu, Wan-Quan; Li, L. (2016)
      In this paper, we propose a robust facial landmarking scheme for frontal faces which can be applied on both controlled and uncontrolled environ-ment. This scheme is based on improvement/extension of the tree-structured ...
    • Deep, dense and accurate 3D face correspondence for generating population specific deformable models
      Gilani, S.; Mian, A.; Eastwood, Peter (2017)
      © 2017 Elsevier Ltd We present a multilinear algorithm to automatically establish dense point-to-point correspondence over an arbitrarily large number of population specific 3D faces across identities, facial expressions ...
    • A novel landmark detector system for multi resolution frontal faces
      Liang, A.; Wang, C.; Liu, Wan-Quan; Li, Ling (2015)
      In this paper, we implement a facial landmarking system to improve the performance of landmark location accuracy for the tree-structured based facial detector proposed recently by Zhu and Ramanan. Our main objective is ...
    Advanced search

    Browse

    Communities & CollectionsIssue DateAuthorTitleSubjectDocument TypeThis CollectionIssue DateAuthorTitleSubjectDocument Type

    My Account

    Admin

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Follow Curtin

    • 
    • 
    • 
    • 
    • 

    CRICOS Provider Code: 00301JABN: 99 143 842 569TEQSA: PRV12158

    Copyright | Disclaimer | Privacy statement | Accessibility

    Curtin would like to pay respect to the Aboriginal and Torres Strait Islander members of our community by acknowledging the traditional owners of the land on which the Perth campus is located, the Whadjuk people of the Nyungar Nation; and on our Kalgoorlie campus, the Wongutha people of the North-Eastern Goldfields.