Local Evidence Aggregation for Regression-Based Facial Point Detection

Brais Martinez, Michel F. Valstar, Xavier Binefa, Maja Pantic

    Research output: Contribution to journalArticleAcademicpeer-review

    94 Citations (Scopus)

    Abstract

    We propose a new algorithm to detect facial points in frontal and near-frontal face images. It combines a regression-based approach with a probabilistic graphical model-based face shape model that restricts the search to anthropomorphically consistent regions. While most regression-based approaches perform a sequential approximation of the target location, our algorithm detects the target location by aggregating the estimates obtained from stochastically selected local appearance information into a single robust prediction. The underlying assumption is that by aggregating the different estimates, their errors will cancel out as long as the regressor inputs are uncorrelated. Once this new perspective is adopted, the problem is reformulated as how to optimally select the test locations over which the regressors are evaluated. We propose to extend the regression-based model to provide a quality measure of each prediction, and use the shape model to restrict and correct the sampling region. Our approach combines the low computational cost typical of regression-based approaches with the robustness of exhaustive-search approaches. The proposed algorithm was tested on over 7,500 images from five databases. Results showed significant improvement over the current state of the art.
    Original languageUndefined
    Pages (from-to)1149-1163
    Number of pages15
    JournalIEEE transactions on pattern analysis and machine intelligence
    Volume35
    Issue number5
    DOIs
    Publication statusPublished - May 2013

    Keywords

    • object detection
    • EWI-24254
    • METIS-302616
    • IR-89547
    • Support Vector Regression
    • HMI-HF: Human Factors
    • Facial point detection
    • probabilistic graphical networks

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