3D assisted face recognition via progressive pose estimation

Wuming Zhang, Di Huang, Dimitris Samaras, Jean Marie Morvan, Yunhong Wang, Liming Chen

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

    6 Scopus citations

    Abstract

    Most existing pose-independent Face Recognition (FR) techniques take advantage of 3D model to guarantee the naturalness while normalizing or simulating pose variations. Two nontrivial problems to be tackled are accurate measurement of pose parameters and computational efficiency. In this paper, we introduce an effective and efficient approach to estimate human head pose, which fundamentally ameliorates the performance of 3D aided FR systems. The proposed method works in a progressive way: firstly, a random forest (RF) is constructed utilizing synthesized images derived from 3D models; secondly, the classification result obtained by applying well-trained RF on a probe image is considered as the preliminary pose estimation; finally, this initial pose is transferred to shape-based 3D morphable model (3DMM) aiming at definitive pose normalization. Using such a method, similarity scores between frontal view gallery set and pose-normalized probe set can be computed to predict the identity. Experimental results achieved on the UHDB dataset outperform the ones so far reported. Additionally, it is much less time-consuming than prevailing 3DMM based approaches.

    Original languageEnglish (US)
    Title of host publication2014 IEEE International Conference on Image Processing, ICIP 2014
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages728-732
    Number of pages5
    ISBN (Electronic)9781479957514
    DOIs
    StatePublished - Jan 28 2014

    Publication series

    Name2014 IEEE International Conference on Image Processing, ICIP 2014

    Bibliographical note

    Publisher Copyright:
    © 2014 IEEE.

    Keywords

    • 3D morphable model
    • asymmetric face recognition
    • pose estimation
    • random forest

    ASJC Scopus subject areas

    • Computer Vision and Pattern Recognition

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