Learning-based Face Synthesis for Pose-Robust Recognition from Single Image

Akshay Asthana, Conrad Sanderson, Tom Gedeon, Roland Goecke

    Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

    36 Citations (Scopus)


    Face recognition in real-world conditions requires the ability to deal with a number
    of conditions, such as variations in pose, illumination and expression. In this paper, we
    focus on variations in head pose and use a computationally efficient regression-based
    approach for synthesising face images in different poses, which are used to extend the
    face recognition training set. In this data-driven approach, the correspondences between
    facial landmark points in frontal and non-frontal views are learnt offline from manually
    annotated training data via Gaussian Process Regression. We then use this learner to
    synthesise non-frontal face images from any unseen frontal image. To demonstrate the
    utility of this approach, two frontal face recognition systems (the commonly used PCA
    and the recent Multi-Region Histograms) are augmented with synthesised non-frontal
    views for each person. This synthesis and augmentation approach is experimentally
    validated on the FERET dataset, showing a considerable improvement in recognition
    rates for ±40◦ and ±60◦ views, while maintaining high recognition rates for ±15◦
    and ±25◦ views.
    Original languageEnglish
    Title of host publicationBritish Machine Vision Conference 2009
    EditorsAndrea Cavallaro, Simon Prince, Daniel Alexander
    Place of PublicationLondon, UK
    PublisherBritish Machine Vision Association
    Number of pages10
    Publication statusPublished - 2009
    EventBritish Machine Vision Conference 2009 - London, United Kingdom
    Duration: 7 Sept 200910 Sept 2009


    ConferenceBritish Machine Vision Conference 2009
    Country/TerritoryUnited Kingdom


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