Random faces guided sparse many-to-one encoder for pose-invariant face recognition

Yizhe Zhang, Ming Shao, Edward K. Wong, Yun Fu

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    One of the most challenging task in face recognition is to identify people with varied poses. Namely, the test faces have significantly different poses compared with the registered faces. In this paper, we propose a high-level feature learning scheme to extract pose-invariant identity feature for face recognition. First, we build a single-hidden-layer neural network with sparse constraint, to extract pose-invariant feature in a supervised fashion. Second, we further enhance the discriminative capability of the proposed feature by using multiple random faces as the target values for multiple encoders. By enforcing the target values to be unique for input faces over different poses, the learned high-level feature that is represented by the neurons in the hidden layer is pose free and only relevant to the identity information. Finally, we conduct face identification on CMU Multi-PIE, and verification on Labeled Faces in the Wild (LFW) databases, where identification rank-1 accuracy and face verification accuracy with ROC curve are reported. These experiments demonstrate that our model is superior to other state-of-the-art approaches on handling pose variations.

    Original languageEnglish (US)
    Title of host publicationProceedings - 2013 IEEE International Conference on Computer Vision, ICCV 2013
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages2416-2423
    Number of pages8
    ISBN (Print)9781479928392
    DOIs
    StatePublished - 2013
    Event2013 14th IEEE International Conference on Computer Vision, ICCV 2013 - Sydney, NSW, Australia
    Duration: Dec 1 2013Dec 8 2013

    Publication series

    NameProceedings of the IEEE International Conference on Computer Vision

    Other

    Other2013 14th IEEE International Conference on Computer Vision, ICCV 2013
    CountryAustralia
    CitySydney, NSW
    Period12/1/1312/8/13

    Keywords

    • Face Recognition
    • Many-to-One Encoder
    • Pose-Invariant
    • Random Faces
    • Sparse

    ASJC Scopus subject areas

    • Software
    • Computer Vision and Pattern Recognition

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  • Cite this

    Zhang, Y., Shao, M., Wong, E. K., & Fu, Y. (2013). Random faces guided sparse many-to-one encoder for pose-invariant face recognition. In Proceedings - 2013 IEEE International Conference on Computer Vision, ICCV 2013 (pp. 2416-2423). [6751411] (Proceedings of the IEEE International Conference on Computer Vision). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCV.2013.300