Fast and exact Newton and Bidirectional fitting of Active Appearance Models

Jean Kossaifi, Georgios Tzimiropoulos, Maja Pantic

    Research output: Contribution to journalArticleAcademicpeer-review

    9 Citations (Scopus)
    72 Downloads (Pure)


    Active Appearance Models (AAMs) are generative models of shape and appearance that have proven very attractive for their ability to handle wide changes in illumination, pose and occlusion when trained in the wild, while not requiring large training dataset like regression-based or deep learning methods. The problem of fitting an AAM is usually formulated as a non-linear least squares one and the main way of solving it is a standard Gauss-Newton algorithm. In this paper we extend Active Appearance Models in two ways: we first extend the Gauss-Newton framework by formulating a bidirectional fitting method that deforms both the image and the template to fit a new instance. We then formulate a second order method by deriving an efficient Newton method for AAMs fitting. We derive both methods in a unified framework for two types of Active Appearance Models, holistic and part-based, and additionally show how to exploit the structure in the problem to derive fast yet exact solutions. We perform a thorough evaluation of all algorithms on three challenging and recently annotated inthe- wild datasets, and investigate fitting accuracy, convergence properties and the influence of noise in the initialisation. We compare our proposed methods to other algorithms and show that they yield state-of-the-art results, out-performing other methods while having superior convergence properties.
    Original languageEnglish
    Pages (from-to)1040-1053
    Number of pages14
    JournalIEEE transactions on image processing
    Issue number2
    Publication statusPublished - Feb 2017
    EventIEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada
    Duration: 27 Sep 201530 Sep 2015


    • HMI-HF: Human Factors
    • inverse compositional
    • bidirectional image alignment
    • EC Grant Agreement nr.: FP7/611153
    • IR-104079
    • Active Appearance Models
    • Newton method
    • forward additive
    • EWI-27551


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