3D facial geometric features for constrained local model

Shiyang Cheng, Stefanos Zafeiriou, Ashish Asthana, Akshay Asthana, Maja Pantic

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    20 Citations (Scopus)
    237 Downloads (Pure)


    We propose a 3D Constrained Local Model framework for deformable face alignment in depth image. Our framework exploits the intrinsic 3D geometric information in depth data by utilizing robust histogram-based 3D geometric features that are based on normal vectors. In addition, we demonstrate the fusion of intensity data and 3D features that further improves the facial landmark localization accuracy. The experiments are conducted on publicly available FRGC database. The results show that our 3D features based CLM completely outperforms the raw depth features based CLM in term of fitting accuracy and robustness, and the fusion of intensity and 3D depth feature further improves the performance. Another benefit is that the proposed 3D features in our framework do not require any pre-processing procedure on the data.
    Original languageUndefined
    Title of host publicationProceedings of IEEE International Conference on Image Processing (ICIP 2014)
    Place of PublicationUSA
    Number of pages5
    ISBN (Print)978-1-4799-5751-4
    Publication statusPublished - Oct 2014
    EventIEEE International Conference on Image Processing, ICIP 2014 - Paris, France
    Duration: 27 Oct 201430 Oct 2014

    Publication series

    PublisherIEEE Computer Society


    ConferenceIEEE International Conference on Image Processing, ICIP 2014
    Abbreviated titleICIP
    Internet address


    • EWI-25826
    • HMI-HF: Human Factors
    • deformable face alignment
    • IR-95232
    • Constrained Local Model
    • 3D facial geometry
    • METIS-309951
    • histogram-based 3D feature

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